Systems, devices, and methods for medication administration guidance

Abstract

Systems, devices, and methods are provided for titrating a medication dose for a patient or user. Titrating may be based on determining the user's risk of hypoglycemia for multiple time periods. Determining the risk of hypoglycemia may be accomplished by known methods, including glucose pattern analysis, glucose dysregulation analysis, low alarm frequency analysis, and combinations thereof. The system may then select a recommended action based on the test substance pattern type. The system may then store an indicator of the recommended action in computer memory for output. The system may then output the recommended action. The recommended action may be output to the user, a healthcare professional, or a caregiver. The recommended action may also vary depending on the person receiving the recommended action.

Description

[Technical Field]

[0001] The subject matter described herein relates generally to systems, devices, and methods for medication guidance, such as, for example, determining insulin doses for the treatment of elevated glucose levels due to diabetes. [Background technology]

[0002] Detecting and / or monitoring analyte levels, such as glucose, ketones, lactate, oxygen, and hemoglobin A1C, can be critical to the health of individuals with diabetes. Patients with diabetes mellitus are at risk for complications, including loss of consciousness and cardiovascular disease, retinopathy, neuropathy, and nephropathy. Diabetic patients generally need to monitor their glucose levels to ensure they remain within a clinically safe range and can also use this information to determine whether and when they need to lower their glucose levels with insulin or when they need to increase their glucose levels with additional glucose.

[0003] Additionally, clinical data is emerging that shows a strong correlation between frequency of glucose monitoring and glycemic control. Despite this correlation, many individuals diagnosed with diabetes do not monitor their glucose levels as frequently as they should due to a combination of factors including the hassle, caution about testing, and the pain and expense associated with glucose testing.

[0004] For patients who rely on the administration of medication (e.g., insulin) to treat or manage diabetes, it would be desirable to have a system, device, or method that can automatically utilize glucose information collected by an analyte monitoring system to provide medication dosing guidance as needed in an easily accessible manner. It would be even more desirable for such a system, device, or method to take into account the physiology, diet, activity, and / or behavior of the user or patient being treated when providing such medication dosing guidance, which may improve accuracy and reliability. Furthermore, in some situations, it would also be desirable for such a system, device, or method to be capable of automatically delivering a selected medication dose.

[0005] The current American Diabetes Association (ADA) standard of care is vague about when and how basal insulin should be titrated to improve glucose control and prescribes care providers to use "evidence-based titration methods." Clinically evaluated basal insulin titration methods are typically based on individual fingerstick blood glucose measurements taken in the fasting state. Dosing decisions based on such sparse data can be problematic, especially for medications like long-acting basal insulin, whose physiological effects can manifest up to 42 hours after administration. Indeed, if titration methods rely solely on morning fasting blood glucose measurements, acute reactions like nocturnal hypoglycemia may not be recorded. Furthermore, blood glucose monitoring poses a burden to patients.

[0006] For these and other reasons, there is a need for improved systems, methods, and devices for medication administration guidance. Summary of the Invention

[0007] Provided herein are exemplary embodiments of systems, devices, and methods for providing, and in some embodiments, delivering, medication dosing guidance. According to one aspect, many of the embodiments described herein comprise a dose guidance system (DGS) including a display device, a sensor control device, and a medication delivery device. The dose guidance system can include a dose guidance application (e.g., software) capable of determining and outputting dose guidance (e.g., recommendations regarding doses, corrections, and dose setting) to the patient. Furthermore, according to some embodiments, the dose guidance system can learn the patient's dosing strategy during a learning period, during which the dose guidance system can estimate key dosing parameters. According to some embodiments, the dose guidance system can also provide guidance for dose setting and corrections after the patient's current dosing strategy has been set in the system. The dose guidance system can also provide guidance regarding basal insulin dosing and adjustments. Exemplary system and safety features of the dose guidance system are also described.

[0008] In one embodiment, the system includes an automatic titration application that can provide dosage change recommendations directly to the patient or healthcare professional. In some embodiments, the automatic titration application can have inputs over which the healthcare professional has some control, such as an upper limit on the maximum dosage that can be recommended, the amount of insulin that can be changed per adjustment, and / or the type of insulin. In addition, the automatic titration application can have additional outputs to the healthcare professional, such as an indication that the maximum recommended dosage has been reached, that optimal titration has been achieved and the patient is in good glucose control, and / or that optimal titration has been achieved but the patient remains in poor glucose control and may require intensified therapy. These outputs can be communicated to the healthcare professional in a number of ways, including a) being sent electronically, b) being displayed in an app to the patient (with guidance to notify the patient's healthcare professional), and / or c) being provided as a report that the healthcare professional can access at any time.

[0009] Many of the embodiments provided herein include improved software features or graphical user interfaces for use in analyte monitoring systems that are highly intuitive, user-friendly, and provide rapid access to a user's physiological information. More specifically, these embodiments enable a user (or healthcare professional) to quickly determine appropriate medication therapy based on information about the user's physiological state, historical administration patterns, and other factors, eliminating the need for the user (or healthcare professional) to tediously review extensive analyte data. Furthermore, some GUIs and GUI functionality enable users (and their caregivers) to better understand and improve their administration patterns and subsequent hypoglycemic and hyperglycemic episodes. Similarly, many other embodiments provided herein include improved software functionality for administration guidance systems that enhance the quality of administration guidance provided to users by enabling safe dosing strategies that minimize hypoglycemic episodes and that take into account real-world events that affect administration strategies.

[0010] The system described herein includes a CGM-based algorithm for basal insulin titration that can improve current methods by comprehensively analyzing a user's glucose for improved dosing optimization. A possible clinical implementation for the algorithm is also presented. Given the impact of clinical inertia and delayed treatment intensification on poor outcomes in type 2 diabetes, tools to assist patients and their caregivers in dosing titration represent an important advancement for CGM-based diabetes management.

[0011] Systems, devices, and methods are provided for titrating a medication dose for a patient or user. Titrating may be based on determining the user's risk of hypoglycemia for multiple time periods. Determining the risk of hypoglycemia may be accomplished by known methods, including glucose pattern analysis, glucose dysregulation analysis, low alarm frequency analysis, and combinations thereof. The system may then select a recommended action based on the test substance pattern type. The system may then store an indicator of the recommended action in computer memory for output. The system may then output the recommended action. The recommended action may be output to the user, a healthcare professional, or a caregiver. The recommended action may also vary depending on the person receiving the recommended action.

[0012] Other systems, devices, methods, features, and advantages of the subject matter described herein will be apparent to one of ordinary skill in the art or will become apparent upon examination of the following figures and detailed description. It is intended that all such additional systems, devices, methods, features, and advantages be included herein, be within the scope of the subject matter described herein, and be protected by the accompanying claims. Features of the example embodiments should not be construed as limiting the scope of the appended claims, unless expressly recited in the claims.

[0013] Details of the subject matter described herein, both in terms of its structure and operation, will become apparent from a review of the accompanying drawings, in which like reference numerals refer to like parts throughout the drawings. The drawings are not necessarily drawn to scale, with emphasis instead being placed upon illustrating the principles of the subject matter. Furthermore, the drawings are intended to convey concepts, and detailed attributes such as relative size and shape may be shown diagrammatically and not accurately. [Brief explanation of the drawings]

[0014] [Figure 1A] FIG. 1 is a block diagram of an exemplary embodiment of an administration guidance system. [Figure 1B] FIG. 1 is a block diagram of an exemplary embodiment of an administration guidance system. [Figure 2A] FIG. 1 is a schematic diagram illustrating an exemplary embodiment of a sensor control device. [Figure 2B] FIG. 1 is a block diagram illustrating an exemplary embodiment of a sensor control device. [Figure 3A] 1A and 1B are schematic diagrams illustrating exemplary embodiments of a drug delivery device. [Figure 3B] 1 is a block diagram illustrating an exemplary embodiment of a medication delivery device. [Figure 4A] FIG. 1 is a schematic diagram illustrating an exemplary embodiment of a display device. [Figure 4B] FIG. 1 is a block diagram illustrating an exemplary embodiment of a display device. [Figure 5] FIG. 1 is a block diagram illustrating an exemplary embodiment of a user interface device. [Figure 6A] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a process flow of operation by a dosing guidance application for assessing meal bolus dosing for multiple daily injection (MDI) dosing therapy. [Figure 6B] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a process flow for operation by a dosing guidance application for glucose pattern analysis (GPA). [Figure 6C] FIG. 10 illustrates an exemplary embodiment of a graph showing information for determining hypoglycemic risk and other metrics of GPA. [Figure 7A] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a process flow for operation by a dosing guidance application for glucose dysregulation analysis. [Figure 7B]FIG. 1 is a flow diagram illustrating an exemplary embodiment of a process flow for operation by a dosing guidance application for glucose dysregulation analysis. [Figure 8A] FIG. 10 is a flow diagram illustrating an exemplary embodiment of a process flow for operation by a dosing guidance application for low alarm frequency analysis. [Figure 8B] FIG. 10 is a flow diagram illustrating an exemplary embodiment of a process flow for operation by a dosing guidance application for low alarm frequency analysis. [Figure 9] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a process flow for operation by a dosing guidance application based on multiple analysis methods. [Figure 10] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a process flow for operation by a dosing guidance application to determine optimal control. DETAILED DESCRIPTION OF THE INVENTION

[0015] Before describing the subject matter of the present disclosure in detail, it is to be understood that the present disclosure is not limited to particular embodiments described herein, as such may, of course, vary, and the scope of the present disclosure will be limited only by the appended claims. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

[0016] Generally, embodiments of the present disclosure include systems, devices, and methods for medication dosing guidance. Dosing guidance can be based on a wide range of information and categories of information specific to a user, such as the user's current and previous analyte levels, the user's current and previous diet, the user's current and previous physical activity, the user's current and previous dosing history (including dosing logs), and other physiological information about the user. According to one aspect of the embodiments, dosing guidance provided by the systems, devices, and methods of the present disclosure can be based not only on individual categories of information but also on the predicted impact that such categories of information will have on the user's future analyte levels.

[0017] The dose guidance functionality can be implemented as a dose guidance application (DGA) including software and / or firmware instructions stored in the memory of the computing device for execution by at least one processor or processing circuitry of the computing device. The computing device can be owned by a user or healthcare professional (HCP), and the user or healthcare professional can interact with the computing device through a user interface. According to some embodiments, the computing device can be a server or trusted computer system accessible over a network, and the dose guidance software can be presented to the user in the form of an interactive web page. In this case, the presentation of the interactive web page can be via a browser running on a local display device (having a user interface) that is in communication with the server or trusted computer system over the network. In embodiments such as those described above, the dose guidance software can be executed across multiple devices, or can be executed partly on the processing circuitry of the local display device and partly on the processing circuitry of the server or trusted computer system. Thus, those skilled in the art will understand that when a description is made that the administration guidance application performs an action, the performance of such action is made according to instructions stored in computer memory (including instructions hard-coded in read-only memory) that, when executed by at least one processor of at least one computing device, cause the administration guidance application to perform the action described in the instructions. In any case, the action may alternatively be made by hardware (e.g., dedicated circuitry) hardwired to accomplish the action, rather than by instructions stored in memory.

[0018] Furthermore, a system in which an administration guidance application is implemented may be referred to herein as an administration guidance system. An administration guidance system may be configured solely for the purpose of providing administration guidance, or may be a multi-function system having only one aspect of providing administration guidance. For example, in some embodiments, an administration guidance system may further have the functionality of monitoring a user's test substance levels. In some embodiments, an administration guidance system may further have the functionality of delivering a medication to a user, such as using an injection device or infusion device. In some embodiments, an administration guidance system may have both the functionality of monitoring a test substance and the functionality of delivering a medication.

[0019] The embodiments described herein, such as those described above, represent improvements in the field of computer-based dosing determination systems, analyte monitoring systems, and drug delivery systems. Specific features and potential advantages of embodiments of the present disclosure are described in further detail below.

[0020] Before describing embodiments of administration guidance in detail, it is desirable to first describe an example of an administration guidance system in which the administration guidance application can be implemented. Exemplary Embodiments of a Dosage Guidance System 1A is a block diagram illustrating an exemplary embodiment of an administration guidance system 100. In this embodiment, the administration guidance system 100 is capable of providing administration guidance, monitoring one or more test substances, and delivering one or more medications. This multi-functional example illustrates the high level of interconnectivity and performance provided by the system 100. However, the embodiments described herein can omit the test substance monitoring component, the drug delivery component, or both, if desired.

[0021] In this example, the system 100 includes a sensor control device (SCD) 102 configured to collect analyte value information from a user and a sensor for delivering medication to the user. The system includes a medication delivery device (MDD) 152 configured to deliver information to a user, and a display device 120 configured to present information to a user and receive input or information from the user. The structure and function of each device are described in detail below.

[0022] The system 100 is configured to enable highly interconnected and flexible communication between devices. The three devices 102, 120, and 152 can communicate directly with each other (without any intervening electronic devices) or indirectly with each other (through the cloud network 190 or through other devices and the network 190, in that order). In FIG. 1A, bidirectional communication between devices and between the devices and the network 190 is indicated by bidirectional arrows. However, those skilled in the art will understand that one or more devices (e.g., sensor control devices) may also engage in unidirectional communication, such as broadcast, multicast, or advertising. Whether bidirectional or unidirectional, communication may be wired or wireless. Furthermore, the protocols governing communication along each path may be the same or different, proprietary, or standard. For example, wireless communication between devices 102, 120, and 152 may be performed according to the Bluetooth® (including Bluetooth® Low Energy) standard, the Near Field Communication (NFC) standard, the WiFi (802.11x) standard, the mobile telephony standard, etc. All communications over the various paths may be encrypted, and each device shown in FIG. 1A may be configured to encrypt and decrypt communications sent and received. In each case, the communication paths shown in FIG. 1A may be direct (e.g., Bluetooth® or NFC) or indirect (e.g., Internet protocols such as WiFi or cellular). Note that an embodiment of system 100 need not necessarily have communication capabilities that cover all of the paths shown in FIG. 1A.

[0023] 1A shows one each of the display device 120, the sensor control device 102, and the medication delivery device 152, those skilled in the art will understand that the system 100 may include a plurality of any of these devices. For example, by way of example only, the system 100 may include a single sensor control device 102 that communicates with multiple (e.g., two, three, four, etc.) display devices 120 and / or multiple medication delivery devices 152. Alternatively, the system 100 may include multiple sensor control devices 102 that communicate with a single display device 120 and / or a single medication delivery device 152. Furthermore, when multiple devices are included, the types of the devices may be the same or different among the multiple devices. For example, the system 100 may include multiple display devices 120, such as smartphones, handheld receivers, smartwatches, etc., each of which may be capable of communicating with either or both of the sensor control device 102 and the drug delivery device 152, and may be capable of communicating with each other.

[0024] The analyte data may be transferred between devices in system 100 autonomously (e.g., automatically according to a schedule) or in response to a request for the analyte data (e.g., a request for the analyte data is sent from a first device to a second device, and then the second device sends the analyte data to the first device). Other data communication technologies may also be employed to support more complex systems, such as cloud network 190.

[0025] 1B is a block diagram illustrating another exemplary embodiment of a dosing guidance system 100. In this example, the system 100 includes a sensor control device 102, a medication delivery device 152, a first display device 120-1, a second display device 120-2, a local computer system 170, and a trusted computer system 180 accessible via a cloud network 190. The sensor control device 102 and the medication delivery device 152 are configured to communicate with each other and with the display device 120-1, which can function as a communications hub for aggregating information from the sensor control device 102 and the medication delivery device 152, processing the aggregated information, displaying the aggregated information in a desired location, and forwarding some or all of the information to the cloud network 190 and the computer system 170. Conversely, the display device 120-1 can receive information from the cloud network 190 or the computer system 170 and communicate some or all of the received information to the sensor control device 102, the drug delivery device 152, or both. The computer system 170 can be a suitable data processing device, such as a personal computer, a server terminal, a laptop computer, or a tablet terminal. The computer system 170 can include or display software for data management, data analysis, and data communication with components within the system 100. A user or medical personnel can use the computer system 170 to display and analyze analyte data measured by the sensor control device 102. Note that while FIG. 1B shows one sensor control device 102, one drug delivery device 152, and two display devices 120-1 and 120-2, those skilled in the art will understand that the system 100 may include multiple of any of these devices, and if multiple devices are included, the devices may be the same or different types.

[0026] 1B , according to some embodiments, trusted computer system 180 may reside, either physically or virtually via a secure connection, under the ownership of a manufacturer or distributor of components of system 100. Trusted computer system 180 may be used to authenticate devices (e.g., devices 102, 120-n, 152) of system 100 for securely storing user data, and may also serve as a server providing data analysis programs (e.g., accessible via a web browser) for performing analysis of users' analyte measurement data and medication histories. Trusted computer system 180 may also function as a data hub for data routing and exchange among all devices communicating with system 180 via cloud network 190. In other words, all devices of system 100 configured to communicate with cloud network 190 (e.g., directly via an internet connection or indirectly via other devices) may also communicate, directly or indirectly, with all other devices of system 100 configured to communicate with cloud network 190.

[0027] In the figure, display device 120-2 is shown in communication with cloud network 190. In this example, device 120-2 may be owned by another user who is authorized to access the analyte and medication data of the wearer of sensor control device 102. For example, the owner of display device 120-2 may be, in one example, the parent of a child wearing sensor control device 102, or, in another example, the caregiver of an elderly patient wearing sensor control device 102. System 100 may be configured to communicate analyte and medication data about the wearer via cloud network 190 (e.g., communicating via trusted computer system 180) to other users who are authorized to access the data.

[0028] Exemplary Embodiments of an Analyte Monitoring Device The test substance monitoring functionality of the administration guidance system 100 may be achieved by including one or more devices configured to collect, process, and display a user's test substance data. Exemplary embodiments of such devices and methods of use are described in WO 2018 / 152241 and U.S. Patent Application Publication No. 2011 / 0213225, the entire contents of which are incorporated herein by reference for all purposes.

[0029] Analyte monitoring can be accomplished in a variety of ways. For example, a "Continuous Analyte Monitoring" device (e.g., a "Continuous Glucose Monitoring (CGM)" device) is a device that can continuously or repeatedly transmit data (e.g., automatically according to a schedule) from a sensor controlling device to a display device, with or without prompting. Another example is a "Flash Analyte Monitoring" device (e.g., a "Flash Glucose Monitoring" device or simply a "Flash" device), which can transfer data from the sensor controlling device in response to a user request for data received from the display device using Near Field Communication (NFC) or Radio Frequency Identification (RFID) protocols.

[0030] Analyte monitoring devices that utilize sensors configured for placement partially or entirely within a user's body may be referred to as in vivo analyte monitoring devices. For example, an in vivo sensor may be placed within a user's body so that at least a portion of the sensor is in contact with bodily fluid (e.g., interstitial fluid (“ISF”), such as dermal fluid within the dermis layer or subcutaneous fluid deeper than the dermis layer, or blood) to measure the analyte concentration in the bodily fluid. In vivo sensors may use various types of sensor technologies (e.g., chemical, electrochemical, or optical). Some systems utilizing in vivo analyte sensors may operate without the need for finger-prick calibration.

[0031] On the other hand, an "in vitro" system is a system in which a sensor is contacted with a biological sample outside the body (more precisely, "ex vivo"). Systems include a port for receiving an analyte test strip carrying a user's bodily fluid, which can then be analyzed to determine the user's blood glucose level. Other ex vivo devices have also been proposed that attempt to measure analyte levels in a user's body non-invasively, for example, by using optical techniques that can measure analyte levels in the body without mechanically penetrating the user's body or skin. Many in vivo and ex vivo devices also have in vitro capabilities (e.g., in vivo display devices that also include a test strip port).

[0032] While the present subject matter will be described below through a description of a sensor configured to measure glucose concentrations, the detection and measurement of other analyte concentrations is also within the scope of the present disclosure. These other analytes may include, for example, ketones, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, DNA, fructosamine, glutamine, growth hormone, hormones, peroxides, prostate-specific antigen, prothrombin, RNA, thyroid-stimulating hormone, troponin, and the like. Drug concentrations may also be monitored, for example, antibiotics (e.g., gentamicin, vancomycin, etc.), digitoxin, digoxin, drugs of abuse, theophylline, warfarin, and the like. The sensor may be configured to measure two or more different analytes simultaneously or non-simultaneously. In some embodiments, a sensor control device can be coupled to two or more sensors, with one of the sensors configured to measure a first analyte (e.g., glucose) and one or more other sensors configured to measure one or more different analytes (e.g., any of the analytes described herein). In other embodiments, a user can wear two or more sensor control devices configured to measure different analytes.

[0033] The embodiments described herein may be applied to any type of in vivo, in vitro, or ex vivo device configured to monitor analytes such as those described above.

[0034] In many embodiments, the sensor control device 102 controls the operation of the sensor. The sensor can be mechanically and communicatively coupled to the sensor control device 102 or can be communicatively coupled to the sensor control device 102 using wireless communication techniques. The sensor control device 102 can include electronic components and a power source that enable the sensor to detect and control the analyte. In some embodiments, the sensor or sensor control device 102 can be self-powered, eliminating the need for a battery. The sensor control device 102 can also include communication circuitry for communicating with other devices (e.g., a display device). Such other devices may or may not be near the user's body. The sensor control device 102 can be stationary on the user's body (e.g., placed on the user's skin, such as by being attached to the user's skin, or carried in the user's clothing). The sensor control device 102 can also be implanted within the user's body along with the sensor. The functionality of the sensor control device 102 may be divided into a first component (e.g., a component that performs sensor control) that is implanted within the body and a second component (e.g., a relay component that communicates with the first component and with external devices such as a computer or smartphone) that resides external to the body. In other embodiments, the sensor control device 102 may be external to the body and configured to non-invasively measure analyte levels of a user. Depending on the actual implementation or embodiment, the sensor control device may also be referred to as a “sensor control unit,” an “on-body electronics” device or unit, an “on-body” device or unit, an “in-body electronics” device or unit, an “in-body” device or unit, or a “sensor data communication” device or unit, as just a few examples.

[0035] In some embodiments, the sensor control device 102 includes a user interface (e.g., a touch panel) configured to process analyte data and display the resulting analyte values ​​to the user. In such cases, the dosing guidance embodiments described herein may be implemented directly in the sensor control device 102, in whole or in part. However, in many embodiments, it may be desirable to have a display device interconnected to the sensor control device that allows the user to view the analyte values, for example, to minimize the physical form factor of the sensor control device 102 (e.g., to minimize the portion of the device visible on the user's body) or because the sensor control device may not be accessible to the user (e.g., if the device is entirely implanted).

[0036] FIG. 2A is a side view of an exemplary embodiment of a sensor control device 102. The sensor control device 102 can include a housing (mount) 103 for sensor electronics (FIG. 2B), which can be electrically coupled to an analyte sensor 101, which in this example is configured as an electrochemical sensor. According to some embodiments, the sensor 101 can be configured to be placed partially within a user's body (e.g., through the outermost surface of the skin) where it is in fluid contact with the user's bodily fluids and, in conjunction with the sensor electronics, to measure analyte-related data from the user. The housing 103 can be secured to the user's skin using an attachment structure 105, such as an adhesive patch. The sensor 101 can extend through the attachment structure 105 and protrude outside the housing 103. One skilled in the art will appreciate that other forms of attachment to the body and the housing 103 can be used in addition to or instead of adhesive, and all such other attachment forms are within the scope of the present disclosure.

[0037] The sensor control device 102 can be attached to the body in any desired manner. For example, an insertion device (not shown) (sometimes called an applicator) can be used to pierce all or part of the analyte sensor 101 through the outer surface of the user's skin and place it in contact with the user's bodily fluids. Similarly, the insertion device can also be used to place the sensor control device 102 on the skin. In other embodiments, the sensor 101 can be first placed with the insertion device, and then associated electronic components (e.g., wireless transmission circuitry, data processing circuitry, etc.) can be coupled to the sensor 101 (e.g., inserted into a mount). Such electronic component coupling can be performed manually or with the aid of a mechanical device. Examples of insertion devices are described in U.S. Patent Application Publication Nos. 2008 / 0009692, 2011 / 0319729, 2015 / 0018639, 2015 / 0025345, 2015 / 0173661, and 2018 / 0235520, the entire contents of which are incorporated herein by reference for all purposes.

[0038] FIG. 2B is a block diagram illustrating an exemplary embodiment of a sensor control device 102 having an analyte sensor 101 and sensor electronics 104. The sensor electronics 104 can be implemented on one or more semiconductor chips (e.g., an application specific integrated circuit (ASIC), a processor or controller, memory, a programmable gate array, etc.). In the embodiment of FIG. 1B, the sensor electronics 104 includes multiple higher-level functional units, including an analog front end (AFE) 110, a power supply 111, processing circuitry 112, memory 114, timing circuitry 115 (e.g., an oscillator and phase locked loop for providing timing information, such as a clock, to each component of the sensor control device 102), and communication circuitry 116. The AFE 110 provides an analog interface with the sensor 101 and is configured to convert the signal from analog to digital, digital to analog, or both (e.g., via an A / D converter). The power supply 111 is configured to provide power to each component of the sensor control device 102. The communication circuitry 116 is configured to communicate with one or more devices external to the sensor control device 102 (e.g., the display device 120, the drug delivery device 152, or both) in a wired, wireless, or both manner.

[0039] The sensor control device 102 may be implemented with a high degree of interconnectivity, i.e., a power source 111 is coupled to each component shown in Figure 2B, and each of the components responsible for communicating or receiving data, information, or commands (e.g., AFE 110, processing circuitry 112, memory 114, timing circuitry 115, communication circuitry 116) may be communicatively coupled to every other component responsible for such function, for example, via one or more communication connections (i.e., bus) 118.

[0040] The processing circuitry 112 may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be separate chips or may be distributed across (and portions of) many different chips. The processing circuitry 112 may include on-board memory. The processing circuitry 112 may interconnect with the communications circuitry 116 to perform functions such as analog-to-digital conversion, encoding and decoding, digital signal processing, and the like, that assist in converting data signals into a form suitable for wireless or wired transmission (e.g., in-phase or quadrature phase). The processing circuitry 112 may also interconnect with the communications circuitry 116 to perform functions in the opposite direction, i.e., functions necessary to receive wireless transmissions and convert them into digital data or information.

[0041] Processing circuit 112 may execute instructions stored in memory 114. These instructions enable processing circuit 112 to process raw analyte data (i.e., pre-processed analyte data) to derive a final calculated analyte value. In some embodiments, the instructions stored in memory 114, when executed, enable processing circuit 112 to process the raw analyte data to determine one or more of a calculated analyte value, a calculated average analyte value within a predetermined time window, a calculated rate of change of the analyte value within a predetermined time window, and a determination of whether the calculated analyte metric exceeds a predetermined threshold condition. The instructions may also cause the processing circuitry 112 to read and process received transmissions, adjust the timing of the timing circuitry 115, process data or information received from other devices (e.g., calibration, encryption, or authentication information received from the display device 120), perform tasks to establish and maintain communications with the display device 120, recognize voice commands from a user, and transmit using the communications circuitry 116. In embodiments in which the sensor control device 102 includes a user interface, the instructions may also cause the processing circuitry 112 to control the user interface, read user input from the user interface, display information on the user interface, format data for display, etc. Note that while the above functionality is described as being coded into the instructions, such functionality may instead be implemented in the sensor control device 102 using a hardware or firmware design that can achieve its functions without relying on the execution of stored software instructions.

[0042] The memory 114 may be shared by one or more of the various functional units present in the sensor control device 102, or may be distributed among two or more of them (e.g., as separate memories present in different chips). The memory 114 may also be a standalone chip in its own right. The memory 114 is non-transitory memory and may be volatile memory (e.g., RAM, etc.) and / or non-volatile memory (e.g., ROM, flash memory, F-RAM, etc.).

[0043] The communications circuitry 116 may be implemented as one or more components (e.g., communications circuitry such as a transmitter, receiver, transceiver, passive circuitry, encoder, decoder, etc.) that perform a function for communication over each communications path or link. The communications circuitry 116 may include or be coupled to one or more antennas for wireless communications.

[0044] The power supply 111 may include one or more batteries, which may be rechargeable or disposable, and may include a power management circuit to control battery charging, monitor the usage of the power supply 111, boost power, convert DC, etc.

[0045] Additionally, an optional temperature sensor (not shown) may collect readings or measurements of temperature on the skin (sensor temperature). The collected readings or measurements may be communicated from the sensor control device 102 to another device (e.g., the display device 120) (either as individual values ​​or as an aggregate of measurements within an aggregation period). However, instead of or in addition to actually outputting temperature measurements to the user, the temperature readings or measurements may be linked to a software routine executed by the sensor control device 102 or the display device 120 to correct or complement the analyte measurements before outputting them to the user.

[0046] Exemplary Embodiments of a Medication Delivery Device The medication delivery functionality of the administration guidance system 100 may be achieved by including one or more medication delivery devices (MDDs) 152. The medication delivery device 152 may be any device configured to administer a particular medication. The medication delivery device 152 may also include a device (e.g., a pen cap) that transmits administration-related data to the administration guidance system but does not itself deliver the medication. The medication delivery device 152 may be configured as a portable injection device (PID) capable of administering a single dose, such as a bolus dose, in a single injection. A portable injection device is essentially a manually operated syringe, where the medication is pre-filled or must be drawn into the syringe from a reservoir prior to injection. However, in many embodiments, the portable injection device includes electronic components for user interaction to deliver the medication. Portable injection devices are often referred to as medication pens, although they do not necessarily have a pen-like appearance. Portable injection devices with user interface electronics are also often referred to as smart pens. Portable injection devices can be used for a single dose and then disposed of, or they can be durable enough to be reused for multiple doses over the course of a day, week, or month. Portable injection devices are popular among users of multiple daily injection (MDI) therapy.

[0047] The drug delivery device may also include a pump and an infusion set. The infusion set includes a cannula tube, at least a portion of which is placed within the recipient's body. The cannula tube is in fluid communication with a pump, which allows repeated small doses of drug to be delivered through the cannula into the recipient's body over time. The infusion set may be attached to the recipient's body using an infusion set applicator, and in many cases, the infusion set remains implanted for 2-3 days or longer. The pump device includes electronic components for interacting with the user to control the gradual infusion of the drug. In both the portable injection device and the pump, the drug may be stored in a drug reservoir.

[0048] The drug delivery device 152 can function as part of a closed-loop system (e.g., an artificial pancreas system that can operate without user intervention), a semi-closed-loop system (e.g., an insulin loop system that can operate with little user intervention, but with user intervention such as confirming dosage changes), or an open-loop system. For example, a diabetic patient's analyte levels are repeatedly and automatically monitored by the sensor control device 102, and that information is used by the dosing guidance embodiments described herein to automatically calculate or determine an appropriate drug dosage to control the diabetic patient's analyte levels, and then administer that dosage to the diabetic patient. This calculation is performed within the drug delivery device 152 or another device in the system 100, and the determined dosage is communicated to the drug delivery device 152.

[0049] In many embodiments, the administration guidance system provided by the embodiments described herein is for a type of insulin (e.g., rapid-acting (RA) insulin, short-acting insulin, intermediate-acting insulin (e.g., NPH insulin), long-acting (LA) insulin, ultra long-acting insulin, and premixed insulins) and the same drug delivery device 152 is used. Types of insulin include human insulin and synthetic insulin analogue formulations, as well as premixed formulations of insulin. However, the administration guidance embodiments described herein and the drug delivery features of the drug delivery device 152 can also be applied to other drugs besides insulin. Such drugs may include, but are not limited to, exenatide, extended-release formulations of exenatide, liraglutide, lixisenatide, semaglutide, pramlintide, metformin, SGLT1-i inhibitors, SGLT2-i inhibitors, and DPP4 inhibitors. Dosage guidance embodiments can also include combination therapies, including, but not limited to, insulin and a glucagon-like peptide 1 receptor agonist (GLP-1RA), and insulin and pramlintide.

[0050] For ease of describing embodiments of the dosing guidance, the medication delivery device 152 is described in many places herein as having the form of a portable injection device, and in particular a smart pen, although those skilled in the art will readily appreciate that the medication delivery device 152 may alternatively be configured as a pen cap, a pump, or any other type of medication delivery device.

[0051] FIG. 3A is a schematic diagram illustrating an exemplary embodiment of a portable injection device, specifically a medication delivery device 152 configured as a smart pen. The medication delivery device 152 can include a housing 154 for electronics, an injection motor, and a medication reservoir (see FIG. 3B) from which medication can be delivered through a needle 156. The housing 154 can include a removable cap or cover 157 that protects the needle 156 when not in use and can be removed for subsequent injections. The medication delivery device 152 can also include a user interface 158. The user interface 158 can be implemented as a single component (e.g., a touch panel for outputting information to a user and receiving input from a user) or as multiple components (e.g., a combination of a touch panel or display with one or more buttons, switches, etc.). The medication delivery device 152 can also include an actuator 159. Actuator 159 can be moved, depressed, touched, or otherwise actuated to initiate delivery of the medication from the internal reservoir through needle 156 and into the recipient's body. In some embodiments, cap 157 and actuator 159 can also include one or more safety mechanisms to prevent removal or actuation to reduce the risk of adverse health effects from medication injection. Details of these safety mechanisms and more are described in U.S. Patent Application Publication No. 2019 / 0343385 (the "'385 Publication"), the entire contents of which are incorporated herein by reference for all purposes.

[0052] FIG. 3B is a block diagram illustrating an exemplary embodiment of a medication delivery device 152 having electronic components 160. Electronic components 160 are coupled to a power source 161 and a powered injection motor 162, which is coupled to the power source 161 and a medication reservoir 163. While needle 156 is shown in fluid communication with reservoir 163, a valve (not shown) may be present between reservoir 163 and needle 156. Reservoir 163 may be a permanent reservoir or may be a removable reservoir that can be replaced with another reservoir containing the same or a different medication. Electronic components 160 may be implemented on one or more semiconductor chips (e.g., an application-specific integrated circuit (ASIC), a processor or controller, memory, a programmable gate array, etc.). In the embodiment of FIG. 3B, electronic components 160 may comprise multiple higher-level functional units. Such higher-level functional units include processing circuitry 164, memory 165, communication circuitry 166, and user interface electronics 168. The communication circuitry 166 is configured to communicate with one or more devices external to the medication delivery device 152 (e.g., the display device 120) via wires, wirelessly, or both.

[0053] The medication delivery device 152 may be implemented with a high degree of interconnectivity, i.e., a power source 161 is coupled to each component shown in Figure 3B, and each of the components responsible for communicating or receiving data, information, or commands (e.g., processing circuitry 164, memory 165, communication circuitry 166) may be communicatively coupled to all other components responsible for such functionality, for example, via one or more communication connections (i.e., bus) 169.

[0054] The processing circuitry 164 may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be separate chips or may be distributed across (and portions of) many different chips. The processing circuitry 164 may include on-board memory. The processing circuitry 164 may interconnect with the communications circuitry 166 to perform functions such as analog-to-digital conversion, encoding and decoding, digital signal processing, and the like, that assist in converting data signals into a form suitable for wireless or wired transmission (e.g., in-phase or quadrature phase). The processing circuitry 164 may also interconnect with the communications circuitry 166 to perform the reverse functions, i.e., functions necessary to receive wireless transmissions and convert them into digital data or information.

[0055] Processing circuitry 164 may execute software instructions stored in memory 165. These instructions may cause processing circuitry 164 to receive a selection or specification of a designated dose from a user (e.g., entered via user interface 158 or received from another device), process commands (e.g., signals from actuator 159) to deliver the designated dose, and control motor 162 to deliver the designated dose. These instructions may also cause processing circuitry 164 to read and process received transmissions, process data or information received from other devices (e.g., calibration information, encryption information, or authentication information received from display device 120), perform tasks to establish and maintain communications with display device 120, recognize voice commands from a user, and transmit them using communications circuitry 166, etc. In embodiments in which the medication delivery device 152 includes a user interface 158, the instructions may also cause the processing circuitry 164 to control the user interface, read user input from the user interface (e.g., input of a medication dose to be administered or input confirming a recommended dosage), display information on the user interface, format data for display, etc. Note that although the above functionality is described as being coded into the instructions, such functionality may instead be implemented in the medication delivery device 152 using hardware or firmware designs that do not rely on the execution of stored software instructions.

[0056] The memory 165 may be shared by one or more of the various functional units present in the drug delivery device 152, or may be distributed among two or more of them (e.g., as separate memories present in different chips). The memory 165 may also be a standalone chip in its own right. The memory 165 is non-transitory memory and may be volatile memory (e.g., RAM, etc.) and / or non-volatile memory (e.g., ROM, flash memory, F-RAM, etc.).

[0057] The communications circuitry 166 may be implemented as one or more components (e.g., communications circuitry such as transmitters, receivers, transceivers, passive circuits, encoders, decoders, etc.) that perform functions for communication over each communications path or link. The communications circuitry 166 may include or be coupled to one or more antennas for wireless communications. Exemplary antenna details are described in the '385 publication, the entire contents of which are incorporated herein by reference for all purposes.

[0058] The power supply 161 can include one or more batteries, which may be rechargeable or disposable, and may include a power management circuit to control battery charging, monitor the usage status of the power supply 161, boost power, convert DC, etc.

[0059] Additionally, the drug delivery device 152 may also include an integrated or attached in-vitro glucose meter, which may include an in-vitro test strip port (not shown) for receiving an in-vitro glucose test strip for performing in-vitro measurements of blood glucose.

[0060] Exemplary Embodiments of a Display Device The display device 120 can be configured to display information about the system 100 to the user and to accept or receive input from the user regarding the system 100. The display device 120 can display recent analyte measurements to the user in any number of formats. The display device can display the user's past analyte values ​​and other metrics representative of the user's analyte information (e.g., time in range (TIR), ambulatory glucose profile (AGP), hypoglycemia risk level, etc.). The display device 120 can display drug delivery information, such as past dosing information, the date and time of dosing, etc. The display device 120 can display notifications, such as alarms or alerts, related to analyte values ​​and drug delivery.

[0061] Display device 120 can be a dedicated device for system 100 (e.g., an electronic device designed and manufactured primarily to interface with analyte sensors and / or drug delivery devices), or it can be a multi-function general-purpose computing device, such as a handheld or portable mobile communication device (e.g., a smartphone or tablet), or a laptop, personal computer, or other computing device. Display device 120 can also be configured as a mobile smart wearable electronics assembly, such as monocular or binocular smart glasses, a smart watch, or a wristband. Display devices and variations thereof may also be referred to as "reader devices," "readers," "handheld electronics" (or handhelds), "portable data processing" devices or units, "information receivers," "receiver" devices or units (or simply receivers), "relay" devices or units, or "remote" devices or units, to name just a few examples.

[0062] 4A is a schematic diagram illustrating an exemplary embodiment of display device 120. In this example, display device 120 includes user interface 121 and housing 124, which holds display device electronics 130 (FIG. 4B). User interface 121 can be implemented as a single component (e.g., a touch panel configured to provide input and output) or multiple components (e.g., a display and one or more devices configured to receive user input). In this embodiment, user interface 121 includes touch panel display 122 (configured to display information and graphical images and to receive user input via touch) and input buttons 123, both of which are coupled to housing 124.

[0063] The display device 120 may store software (e.g., by a manufacturer or downloaded by a user in the form of one or more software packages such as "apps") that interfaces with the sensor control device 102, the medication delivery device 152, and / or the user on the display device 120. Additionally or alternatively, the user interface may be influenced by web pages displayed on internet interfacing software, such as a browser, executable on the display device 120.

[0064] 4B is a block diagram illustrating an exemplary embodiment of a display device 120 including display device electronics 130. In this example, display device 120 includes a user interface 121, processing circuitry 131, memory 125, communication circuitry 126, power supply 127, and timing circuitry 128 (e.g., an oscillator and phase-locked loop circuit for providing timing information, such as a clock, to the components of display device 120). User interface 121 includes a display 122 and input components 123 (e.g., buttons, actuators, touch-sensitive switches, capacitive switches, pressure-sensitive switches, jog wheels, microphones, speakers, etc.). Communication circuitry 126 is configured for one-way or two-way communication with one or more other devices external to display device 120. Each of these components can be implemented individually as one or more devices, or can be combined into a single multi-function device (e.g., processing circuitry 131, memory 125, and communication circuitry 126 integrated on a single semiconductor chip). Display device 120 may be implemented in a highly interconnected manner, such that a power source 127 is coupled to each component shown in FIG. 4B , and each of the components responsible for communicating or receiving data, information, or commands (e.g., user interface 121, processing circuitry 131, memory 125, communications circuitry 126, timing circuitry 128) may be communicatively coupled to all other components responsible for such functionality, for example, via one or more communications connections (i.e., bus) 129. Note that FIG. 4B is a simplified diagram of typical hardware and functionality present in a display device, and those skilled in the art will readily recognize that other hardware and functionality (e.g., codecs, drivers, glue logic) may also be present.

[0065] Processing circuitry 131 may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be separate chips or may be distributed across (and portions of) many different chips. Processing circuitry 131 may include on-board memory. Processing circuitry 131 may interconnect with communications circuitry 126 to perform functions such as analog-to-digital conversion, encoding and decoding, digital signal processing, and the like, that assist in converting data signals into a form suitable for wireless or wired transmission (e.g., in-phase or quadrature phase). Processing circuitry 131 may also interconnect with communications circuitry 126 to perform the reverse functions, i.e., functions necessary to receive wireless transmissions and convert them into digital data or information.

[0066] Processing circuitry 131 may execute software instructions stored in memory 125. These instructions enable processing circuitry 131 to process raw analyte data (i.e., unprocessed analyte data) and derive corresponding analyte values ​​suitable for display to a user. These instructions may also enable processing circuitry 131 to read, process, store, and communicate administration instructions from a user to drug delivery device 152. These instructions may also enable processing circuitry 131 to execute user interface software adapted to present interactive graphical user interface screens to a user for purposes of changing system parameters (e.g., alarm thresholds, notification settings, display preferences, etc.), presenting current and past analyte value information to a user, presenting current and past drug delivery information to a user, collecting other non-analyte information from a user (e.g., information regarding meals consumed, activities performed, medications administered, etc.), and presenting notifications and alarms to a user. The instructions may also cause processing circuitry 131 to transmit using communications circuitry 126, read and process received transmissions, read input from user interface 121 (e.g., entering a drug dose to be administered or approving a recommended dosage), display information on user interface 121, adjust the timing of timing circuitry 128, process data or information received from other devices (e.g., analyte data, calibration information, encryption information, or authentication information received from sensor control device 102), perform tasks to establish and maintain communications with sensor control device 102, recognize voice commands from a user, etc. Note that while the above functionality is described as being coded into the instructions, such functionality may instead be implemented in display device 120 using a hardware or firmware design that is capable of realizing the functionality without relying on the execution of stored software instructions.

[0067] Memory 125 may be shared by one or more of the various functional units present in display device 120, or may be distributed among two or more of them (e.g., as separate memories present in different chips). Memory 125 may also be a standalone chip in its own right. Memory 125 is non-transitory memory and may be volatile memory (e.g., RAM, etc.) and / or non-volatile memory (e.g., ROM, flash memory, F-RAM, etc.).

[0068] The communications circuitry 126 may be implemented as one or more components (e.g., communications circuitry such as a transmitter, receiver, transceiver, passive circuitry, encoder, decoder, etc.) that perform a function for communication over each communications path or link. The communications circuitry 126 may include or be coupled to one or more antennas for wireless communications.

[0069] The power supply 127 may include one or more batteries, which may be rechargeable or disposable, and may include power management circuitry to control battery charging, monitor power supply 127 usage, boost power, convert DC, etc.

[0070] Display device 120 may also include one or more data communication ports (not shown) for wired data communication with external devices such as computer system 170, sensor control device 102, or drug delivery device 152. Display device 120 may also include an integrated or attached in-vitro glucose meter, which may include an in-vitro test strip port (not shown) for receiving in-vitro glucose test strips for performing in-vitro measurements of blood glucose.

[0071] The display device 120 can display analyte measurement data received from the sensor control device 102 and can also be configured to output alarms, alert notifications, glucose values, and the like. Such output can be visual, audible, tactile, or any combination thereof. Additionally, in some embodiments, the sensor control device 102, the drug delivery device 152, or both can be configured to output alarm or alert notifications that are visual, audible, tactile, or any combination thereof. Further details and other display embodiments are described, for example, in U.S. Patent Application Publication No. 2011 / 0193704, the entire contents of which are incorporated herein by reference for all purposes.

[0072] Exemplary Embodiments of Administration Guidance The following exemplary embodiments relate to dose guidance functionality provided by dose guidance system 100. In many embodiments, the dose guidance functionality is implemented as a set of software instructions stored on and / or executed on one or more electronic devices. This dose guidance functionality is referred to herein as a dose guidance application (DGA). In some embodiments, the dose guidance application is stored, executed, and presented to a user on the same single electronic device. In other embodiments, the dose guidance application can be stored and executed on one device and presented to a user on a different electronic device. For example, the dose guidance application may be stored and executed on trusted computer system 180 and presented to a user by a web page displayed via an internet browser executing on display device 120. The dose guidance application may be a standalone application or may be incorporated in whole or in part into another software application. The application may be either a mobile application, a web-based server that supports the mobile application by providing an alternative means of data processing and a communications hub, or a combination of the two.

[0073] The system can be either mobile-based, web-based, or a dual system utilizing both entities. In a dual system, the algorithm can reside in either application. Within the mobile application, the user can receive daily reminders for basal dose administration and / or logging as algorithm input, and / or titration recommendations from the algorithm residing within the mobile application. Glucose data can be calculated and provided directly within the mobile application, or provided to the mobile application from an external source. In the web-based application, the care provider can track the user's glycemic control and, if available, insulin administration habits via reports and receive titration recommendations for review and approval prior to patient notification. In a dual application system, the care provider can "approve" a dosage change recommendation within the web application. This action then immediately pushes a notification of the new dosage to the user's mobile app. In some embodiments, this approval by a healthcare professional may be required before the recommended action is output to the user. In other embodiments, prior approval by a healthcare professional may not be required before the recommended action is output to the user.

[0074] Accordingly, many different embodiments exist regarding the number and type of electronic devices used to store, execute, and present the administration guidance application, or portions thereof, to a user. With respect to user presentation, a device configured to implement user presentation functionality will be referred to herein as a user interface device (UID) 200. FIG. 5 is a block diagram illustrating an exemplary embodiment of a user interface device 200. In this embodiment, the user interface device 200 includes a housing 201 coupled to a user interface 202. The user interface 202 is configured to output information to a user and to input or receive information from the user. In some embodiments, the user interface 202 is a touch panel. As shown, the user interface 202 includes a display 204 (which may be a touch panel) and input components 206 (e.g., buttons, actuators, touch-sensitive switches, capacitive switches, pressure-sensitive switches, jog wheels, a microphone, a touchpad, softkeys, a keyboard, etc.).

[0075] Many of the devices described herein can be implemented as user interface devices 200. For example, in many embodiments, the display device 120 is utilized as the user interface device 200. In some embodiments, the medication delivery device 152 can also be implemented as a user interface device 200. Also, in embodiments in which the sensor control device 102 includes a user interface, the sensor control device 102 can be implemented as a user interface device 200. Furthermore, the computer system 170 can also be implemented as a user interface device 200.

[0076] the purpose The Dose Guidance System (DGS100) utilizes glucose data and additional data, such as typical mealtimes and bedtimes, to adjust insulin doses, such as basal insulin doses. The Dose Guidance System includes an application, such as a smartphone-based mobile application, integrated with a linked insulin pen and continuous glucose sensor to improve treatment management for insulin-dependent people with diabetes (PWD) receiving basal insulin or multiple dose injections (MDI).

[0077] Continuous glucose data and insulin data in various forms (real-time, scanned, historical, and streaming) can serve as inputs for performing all three functions. The administration guidance system 100 can receive glucose data by various means and in various forms, including scanned, historical, and streaming. Scanned data, including recent glucose values ​​and trend values, can be obtained by the user upon request. Historical glucose data can be generated by components of the administration guidance system, which can generate and record glucose values ​​and trend values ​​periodically (e.g., every 15 minutes). Past historical data can be obtained by the user using scans. Streaming data can include glucose values ​​and trend values ​​that are generated and recorded periodically (e.g., every minute) and automatically sent to the administration guidance application. Sensor data can have regular intervals of 1 minute, 5 minutes, 10 minutes, or 15 minutes between readings.

[0078] The system can also receive insulin data from multiple sources. The insulin data can be manually logged or transferred from a medication delivery device 152, such as an insulin pen. The glucose and insulin data can be transferred by any known means, for example, through wireless communication technologies such as Bluetooth or NFC.

[0079] Other aspects of the administration guidance system are described in U.S. Patent Application Publication Nos. 2021 / 0050085 and 2022 / 0249779, the disclosures of all of which are expressly incorporated by reference into this specification for any purpose.

[0080] Glucose pattern analysis for insulin therapy An exemplary embodiment of a method for determining insulin dose settings will now be described. The algorithm may have two classes of input: (1) glucose data and (2) typical mealtimes and bedtimes. The outputs are glucose patterns and hypoglycemia risk for each time period, as defined below. Where appropriate, these outputs are mapped to recommended basal insulin dose settings presented within the application.

[0081] The dosing guidance application may require the user to input or estimate typical meal and bedtime times to define time-of-day (TOD) periods that can be used to analyze test substance data. TOD periods can include overnight, post-breakfast, post-lunch, and post-dinner periods. Daily meal and bedtimes can be either fixed or user-defined and can be entered by either the patient or a caregiver. Bedtimes can be entered by the user or estimated from user-entered meal times. These entered / calculated times can define time-of-day (TOD) periods used to analyze glucose data. Segmentation by mealtime and sleep is valuable in the context of insulin dose titration, as post-prandial glucose windows and overnight fasting windows are important indicators of drug efficacy. In other embodiments, TOD periods can be defined in other ways and do not need to be strictly defined as above for the algorithm to function.

[0082] In some embodiments, the administration guidance application may also receive additional inputs from the user, such as the user's weight, timing and amount of insulin administration, and meal and exercise logs. These additional inputs may be accomplished either manually by logging into the mobile application or in a hands-off manner via communication from a connected device (e.g., a Bluetooth-enabled insulin pen or pen cap, activity monitor, etc.) to the application.

[0083] Glucose values ​​can be collected over multiple days and binned according to the TOD period their timestamps fall in. Once enough data has been collected for all TOD periods, metrics can be calculated for each period to quantify the user's glucose control during each period.

[0084] One or more metrics can be determined for different TOD periods. These one or more metrics can be used by the administration guidance application to determine the likelihood of low glucose (LLG), and the median glucose value can be used to quantify the degree of hypoglycemic risk and hyperglycemic risk, respectively. These metrics can be any glucose-derived metric (e.g., average glucose during the time period, time in target range, or a combination of the two), median glucose value, variability below the median (median minus the 10th percentile), and the likelihood of low glucose (LLG, as defined elsewhere) is considered. Four TOD periods can be defined as overnight, after breakfast, after lunch, and after dinner. The metrics calculated within each period can then be mapped to a glucose period pattern.

[0085] U.S. Patent Application Publication No. 2018 / 0188400 (the '400 Publication), which is incorporated herein by reference for any purpose, describes an example implementation for deriving and determining a risk metric that may be utilized in glucose pattern analysis (GPA) for embodiments of a dosing guidance application. This implementation utilizes, among other things, central tendency (e.g., mean, median, etc.) and variability data over a multi-day period to determine a risk metric that corresponds to a degree of hypoglycemic risk ("hypoglycemic risk"). This embodiment is summarized herein, and a more comprehensive description of this embodiment and its variations can be obtained by reference to the '400 Publication.

[0086] An alternative to the embodiment described in the '400 publication is described in U.S. Patent Application Publication No. 2014 / 0350369, which is also incorporated herein by reference for all purposes. For example, instead of using median and variability, the method can employ any two statistical measures that define the distribution of the data. As described in the '369 publication, statistical measures can be used to determine the glucose target range (e.g., G LOW = 70 mg / dL and G HIGH = 140 mg / dL). Common measures related to the target range are time in target range (TIR), time above target (t AT ), and the time to fall short of the target (t BT ) If the glucose data is modeled as a distribution (e.g., a gamma distribution), then a predetermined threshold G LOW and G HIGH For t AT and t BT For that threshold, the algorithm also calculates t BT_HYPO We can define t BT t BT_HYPO If the patient's blood sugar level exceeds G, the patient may be determined to be at high risk of hypoglycemia. For example, high hypoglycemia risk may be determined to be LOW = 70 mg / dL BTcan be defined as the case where t exceeds 5%. AT_HYPER We can define a metric called t AT t AT_HYPER If the blood glucose level exceeds 100%, the patient can be determined to be at risk of hyperglycemia. The degree of hypoglycemia risk and hyperglycemia risk can be determined by the following equations: LOW or t BT_HYPO , or G HIGH or t AT_HYPER This can be adjusted by adjusting one of the three measures: TIR, t BT , and t AT Any two of these may be used to define the control grid. These alternative (and other) means may be used to determine risk metrics for the administration guidance application embodiments described herein.

[0087] The administration guidance application embodiments described herein can operate based on a quantitative evaluation of a user's test substance data during a time of day (TOD). This quantitative evaluation can be performed in a variety of ways. For example, the embodiments described herein can evaluate the test substance data over a multi-day period to determine one or more metrics that describe the associated risk indicated by the test substance data during the corresponding time of day (TOD). These metrics can then be used to classify the test substance data from the time of day (TOD) period as one of a number of patterns. For example, these patterns may be indicative of common or predominant glucose behaviors or trends during that time of day. Any number of patterns greater than or equal to two may be utilized by the administration guidance application embodiments. For ease of reference herein, these patterns are referred to as glucose pattern types, and although the embodiments described herein refer to implementations that utilize three glucose pattern types (e.g., low pattern, high / low pattern, and high pattern), other implementations may utilize only two types or more than three types, and the types may differ from those described herein.

[0088] For example, a basal insulin dose can be used to initiate the dose titration evaluation. For each TOD period (nighttime, after breakfast, after lunch, and after dinner), the administration guidance application can map the two metrics (LLG and median glucose) described above to four logical “pattern” variables according to the glucose pattern analysis method described below. FIG. 6A illustrates an exemplary method 400 operation by the administration guidance application for evaluating basal dose titration. Method 400 may include, at 402, the administration guidance application determining an analyte pattern type for each TOD period of the plurality of TOD periods by executing a glucose pattern analysis (GPA) algorithm that receives as input time-correlated analyte data generated by a sensor control device worn by the patient over the analysis period. Method 400 may further include, at 404, the administration guidance application executing a recommendation algorithm to select a recommended basal dose based on the analyte pattern types determined for the plurality of TOD periods. The method 400 may further include, at 406, the administration guidance application storing the indicator of the recommended action in computer memory for output to a computing device that administers the medication, such as the user interface device 200 or the medication delivery device 152. The user interface device 200 may use the indicator of the recommended action to control a user interface, for example, by causing a human-readable representation of the indicator to appear on a display or by generating an audio output that expresses the indicator in a human language. The medication delivery device 152 may use the indicator to adjust or maintain the next associated dose administration. Further details of the method 400 are described below.

[0089] 6B is a flow chart illustrating an exemplary embodiment of a glucose pattern analysis method 410 that may be implemented as the glucose pattern analysis algorithm referenced at 402. Method 410 may be performed over a particular TOD period, which may be an entire day (e.g., a 24-hour period), or a time block (e.g., three 8-hour periods), or a portion of a day bounded by user activity (e.g., eating, exercise, sleep, etc.). In many embodiments, the TOD periods may correspond to meals (e.g., after breakfast, lunch, and dinner) and sleep (e.g., overnight). These TOD periods may correspond to fixed times of day when activities typically occur (e.g., after breakfast from 5:00 AM to 10:00 AM), and such time blocks may be set by the user or may rely on determining whether a meal or activity actually occurred by automatic meal or activity detection or by the user's instruction of a meal or activity (e.g., using user interface device 200).

[0090] The dosing guidance application can perform method 410 independently for each TOD period to obtain a separate pattern assessment for that period. At 412, the dosing guidance application can determine central tendency and variability values ​​from the user's test substance data for a particular TOD period. The user's test substance data can be available from the user's own records or the user's medical professional's records, or the user's test substance data can be collected, for example, by the dosing guidance system 100. The test substance data preferably spans a multi-day period (e.g., 2 days, 2 weeks, 1 month, etc.) so that there is enough data within the TOD period to make a reliable determination. In other embodiments, the method can be performed in real time on limited data. The dosing guidance application can use any type of central tendency metric that correlates to the central tendency of the data, including, but not limited to, the median or mean. Any desired variability metric can be used, including, but not limited to, a range of variation that spans the entire data set (e.g., from minimum to maximum), a range of variation that spans a majority of the data but less than the entire data set to reduce the impact of outliers (e.g., from the 90th percentile to the 10th percentile, from the 75th percentile to the 25th percentile), or a range of variation that focuses on a particular asymmetric range (e.g., a low-range variability that can span from at or near the central tendency value to a lower value of the data, e.g., the 25th percentile, the 10th percentile, or the minimum). The metrics selected to represent central tendency and variability can vary depending on the implementation.

[0091] At 414, the administration guidance application can assess a risk of hypoglycemia ("hypoglycemia risk") metric based on the central tendency and variability values. One such method for determining hypoglycemia risk is described in connection with FIG. 6C, which shows an exemplary embodiment of a framework for determining hypoglycemia risk and other metrics. While FIG. 6C is intended to convey the framework to the reader, this framework can be implemented electronically in a number of different ways, such as a software algorithm (e.g., a mathematical formula, a set of if-else statements, etc.), a lookup table, firmware, a combination thereof, or other methods.

[0092] 6C is a graph illustrating the relationship between central tendency and variability (e.g., low-range variability), which can be used to evaluate or identify a region or zone that holds or corresponds to a determined central tendency and variability data pair for a particular TOD. More than one zone can be used. In this embodiment, the data pair can correspond to a target zone 425 or one of three hypoglycemia risk zones: low zone 426, medium zone 428, or high zone 430. A first hypoglycemia risk function (e.g., a curve or linear boundary), referred to as medium risk function 422, distinguishes between low zone 426 and medium zone 428. A second hypoglycemia risk function, referred to as high risk function 424, distinguishes between medium zone 428 and high zone 430. The central tendency and variability data pairs can be evaluated or compared against these zones to determine a hypoglycemia risk metric for the corresponding TOD period.

[0093] The hypoglycemia risk functions 422 and 424 can be implemented explicitly in the administration guidance application as mathematical functions (e.g., polynomials) or can be implemented implicitly, such as by defining each zone by a pair, including using a lookup table, a set of if-else statements, threshold comparisons, or otherwise. The hypoglycemia risk functions 422 and 424 can be pre-loaded into the administration guidance application, downloaded from a trusted computer system 480, or set by another party, such as a healthcare professional. Once implemented in the administration guidance application, the hypoglycemia risk functions 422 and 424 can be treated as fixed or can be adjusted by the user or healthcare professional. Exemplary methods for determining hypoglycemia risk functions are described in the '400 publication.

[0094] At 416, the administration guidance application can assess a hyperglycemic risk metric (“hyperglycemic risk”) based on the central tendency value. In this embodiment, hyperglycemic risk can be assessed by comparing the central tendency value for a particular TOD period to a central tendency target or threshold 432. The magnitude and / or sign of the difference between the central tendency value and the target 432 can identify the degree of hyperglycemic risk. For example, if the central tendency value is below the target 432 (e.g., a negative value), a low hyperglycemic risk can exist. If the central tendency value exceeds the target 432 (e.g., a positive value) but differs by less than a threshold amount (e.g., 5 percent, 10 percent, etc.), a moderate hyperglycemic risk can exist. If the central tendency value exceeds the target 432 and differs by more than the threshold amount, a high hyperglycemic risk can exist. The use of three distinct groupings of hyperglycemic risk (e.g., low, moderate, high) is exemplary, and any number of groupings greater than two can be used.

[0095] In other embodiments, the administration guidance application can evaluate the hyperglycemic risk metric at 416 before evaluating the hypoglycemic risk at 414. Alternatively, in another embodiment, the assessment of hypoglycemic risk at 414 and the assessment of hyperglycemic risk at 416 can occur simultaneously in parallel.

[0096] Other metrics, such as variability risk, may also be evaluated. For example, a variability value less than a first variability threshold 434 may indicate a low variability risk, a variability value greater than the first variability threshold 434 and less than a second variability threshold 436 may indicate a medium variability risk, and a variability value greater than the second variability threshold 436 may indicate a high variability risk. Again, the use of three distinct groupings for variability risk is one example. The administration guidance application may use any number of two or more groupings.

[0097] In step 418, the administration guidance application can determine a pattern type for the TOD period based on the evaluated risk metric or metrics. In one exemplary embodiment, the pattern determination can be evaluated using a hypoglycemia risk metric and a hyperglycemia risk metric. If the hypoglycemia risk metric is high, the pattern can be set as a low pattern (or a "Lows" pattern). Otherwise, if the hypoglycemia risk is moderate and the hyperglycemia risk is either high or moderate, the pattern can be set as a high / low (or moderate) pattern (or a "Lows" pattern with some highs or some lows). Otherwise, if the hyperglycemia risk is high or moderate and the hypoglycemia risk is low, the pattern can be set as a high pattern (or a "Highs" pattern). If both the hyperglycemia risk and the hypoglycemia risk are low, the identified pattern can be acceptable (e.g., an "OK" message is displayed or output) (or "no pattern").

[0098] An exemplary method for determining a glucose period pattern is presented below in pseudocode. if LLG is high then period pattern = LOW elseif (median glucose is moderate or high AND LLG is moderate) then period pattern = HIGH / LOW elseif (median glucose is moderate or high AND LLG is low) then period pattern = HIGH elseif (median glucose is low and LLG is low or moderate) then period pattern = NONE end Thus, method 410 is one example of how the administration guidance application outputs one of a plurality of pattern types for each TOD period. The number of pattern types within the pattern types themselves may vary from those described in this embodiment (e.g., low, high / low, high). Once the pattern type for a TOD period is determined, the administration guidance application can store an indicator of the pattern type in a memory location for use in determining a recommended dose setting. Referring again to FIG. 6A, once the administration guidance application has completed glucose pattern analysis for each associated TOD period at 404, it can proceed to determine a recommended dose setting.

[0099] Based on the pattern analysis, the administration guidance application may recommend adjusting the basal insulin dose. For example, the administration guidance application may recommend increasing the basal insulin dose if a high pattern is detected and no hypoglycemic risk is determined in any other TOD periods. Alternatively, the administration guidance application may recommend decreasing the basal insulin dose if a low pattern is detected in at least one TOD period.

[0100] Glucose dysregulation analysis In an alternative method, the administration guidance application can determine a measure of glucose dysregulation to determine recommended insulin dose settings. The application may include an algorithm that analyzes glucose data to determine patterns of glucose dysregulation and suggests subsequent corrective therapeutic actions.

[0101] In some embodiments, the administration guidance application may count instances of glucose dysregulation. Glucose dysregulation may be determined in various ways. For example, in one embodiment, glucose dysregulation may be a count of instances in which the glucose signal is above or below a threshold, such as above 180 mg / dL or below 70 mg / dL. In another embodiment, glucose dysregulation may be a duration above or below a threshold. In another embodiment, glucose dysregulation may be an area above or below a threshold. The threshold may be either fixed within the algorithm, set by the user, or set by the user's caregiver (e.g., a healthcare professional, parent, or guardian). In another embodiment, the threshold may be the same as a high or low glucose alarm threshold within the user's continuous glucose monitor and associated mobile application, such that the counted events correspond to instances of low or high glucose alarms presented to the user. Such area-based determinations represent a combination of magnitude and duration. Dysregulation instances may be counted and categorized according to a time of day period or within another time period (e.g., instances of a day or instances of a week). In this way, the threshold may be expressed as a rate of occurrence of dysregulation instances within a time period, rather than simply a number of instances. If the user's rate of dysregulation instances exceeds the threshold, a recommended dose may be made by the administration guidance application. In another embodiment, glucose dysregulation may be determined by the number of days in which at least a minimum number of instances of glucose dysregulation, as described above, occurred. The frequency of glucose dysregulation may be used as part of an insulin administration dose-setting algorithm. As used herein, two types of glucose dysregulation may be referred to as high events, when glucose exceeds a threshold, and low events, when glucose falls below a threshold. In one embodiment, a high event may be defined as the moment when glucose exceeds a high threshold (e.g., 180 mg / dL). A low event may be defined as the moment when glucose falls below a low threshold.In other embodiments, a high event may be defined as the first time a high glucose alarm is asserted, and a low event may be defined as the first time a low glucose alarm is asserted. In one embodiment, the insulin dose setting algorithm may include logic that depends on the frequency of low and / or high events, which may be determined in various ways. For example, for basal insulin dose setting, the dose setting algorithm may depend on a count of the number of days in which a low event occurred within the previous seven days. For frequent injection insulin dose setting, the dose setting algorithm may depend on a count of the number of days in which a low and / or high event occurred that began within the time of day associated with a particular insulin administration.

[0102] 7A illustrates an exemplary method 460 performed by a dosing guidance application for assessing basal dose setting. Method 460 may include, at 462, the dosing guidance application executing an algorithm that receives as input time-correlated analyte data generated by a sensor control device worn by the patient over an analysis period to determine a measure of glucose dysregulation for at least one TOD period. Method 460 may further include, at 464, the dosing guidance application selecting a recommended action based on the measure of glucose dysregulation for the at least one TOD period by executing a recommendation algorithm. Method 460 may further include, at 466, the dosing guidance application storing an indicator of the recommended action in computer memory for output to a computing device that administers the medication, such as user interface device 200 or medication delivery device 152. User interface device 200 may use the indicator of the recommended action to control a user interface, for example, by causing a human-readable representation of the indicator to appear on a display or by generating an audio output that expresses the indicator in a human language. The drug delivery device 152 can use the indicator to adjust or maintain the next associated dose administration.

[0103] 7B , the administration guidance application may perform a glucose dysregulation analysis in addition to the glucose pattern analysis to determine a recommended insulin dose setting. Method 480 may include, at 482, the administration guidance application determining a glucose pattern type for each TOD period of the plurality of TOD periods by executing a glucose pattern analysis (GPA) algorithm that receives as input time-correlated analyte data generated by a sensor control device worn by the patient over the analysis period. Method 480 may further include, at 484, the administration guidance application determining a measure of glucose dysregulation for each TOD period of the plurality of TOD periods by executing an algorithm that receives as input time-correlated analyte data generated by a sensor control device worn by the patient over the analysis period. Method 480 may further include, at 486, the administration guidance application executing a recommendation algorithm to select a recommended action based on the glucose pattern type and the measure of glucose dysregulation for at least one TOD period. The method 480 may further include, at step 488, the dosing guidance application storing the indicator of the recommended action in computer memory for output to a computing device that administers the medication, such as the user interface device 200 or the medication delivery device 152. The user interface device 200 may use the indicator of the recommended action to control a user interface, for example, by causing a human-readable representation of the indicator to appear on a display or by generating an audio output that represents the indicator in a human language. The medication delivery device 152 may use the indicator to adjust or maintain the next associated dose administration.

[0104] There are many advantages to using both a glucose pattern analysis algorithm and a glucose dysregulation algorithm to determine recommended actions. Glucose dysregulation analysis complements pattern-based methods by increasing the algorithm's sensitivity to glucose dysregulation. Furthermore, whereas pattern-based methods may require more than five days of data to identify dysregulation, glucose dysregulation counting methods may allow for a faster response time for detecting overt instances of poor control before sufficient data is collected for pattern analysis. Such counting methods may also be extendable beyond basal insulin titration and may be applied to titration of any medication that alters analyte values. One such extension is titration of rapid-acting prandial insulin. Additionally, if multiple administrations of a given prandial dose cause multiple instances of glucose dysregulation, this dysregulation counting method may be employed to complement pattern-based titration methods and improve the responsiveness of the medication titration algorithm.

[0105] Low Alarm Frequency Analysis Tracking of CGM low alarms in drug titration algorithms can also be used to identify and adjust drug-induced hypoglycemia by reducing overly aggressive drug dosing.

[0106] In some embodiments, the dosing guidance application may count the number of low alarms triggered within a certain period of time, such as one week. If the number of low alarms triggered exceeds a threshold (e.g., 1), the low pattern described above with respect to glucose pattern analysis may be implemented, and the recommended action may include recommending a reduced dosage, e.g., reducing the recommended basal or bolus dose. The threshold (i.e., the number of acceptable low alarms per period) may be adjusted as an input value to make the dose setting algorithm more aggressive or gentle, or to reflect the patient's tolerance for low alarms.

[0107] In some embodiments, the administration guidance system may maintain a counter that counts the number of low alarms that occur during a period, such as a TOD period. This counter may be checked against a threshold, for example, 1, 2, 3, 4, or 5. If the threshold is met or exceeded, a low pattern may be entered and a recommended action may be determined.

[0108] There are many advantages to including low alarm analysis in determining recommended actions. The rationale behind this approach is that when patients use an open-label CGM and are aware that their glucose is low, they often "treat" their low glucose by ingesting carbohydrates. As a result, low patterns that would normally be detected by a blinded CGM system may go undetected. If a dose-setting algorithm does not consider low alarm frequency, the algorithm may continue to increase the dose indefinitely until the low alarm frequency exceeds the patient's tolerance. A further concern is that if an actively dosed patient with a high low alarm frequency discontinues use of an open-label CGM for any reason, they may no longer be able to effectively manage low glucose episodes and may experience severe hypoglycemia.

[0109] FIG. 8A illustrates the operation of an exemplary method 500 by a dosing guidance application for evaluating basal dose setting. Method 500 may include, at 502, determining a count of low alarms triggered within a certain time period. The algorithm may receive as input time-correlated analyte data generated by a sensor control device worn by the patient over an analysis period and / or may receive a log or count of low alarms or detected events that meet a low alarm condition. The low alarm condition may be user-configured to activate when a glucose reading falls below a set value (e.g., 70 mg / dL) and may remain on until glucose rises above the set value or the set value plus some buffer. Note that other definitions of low alarm may apply herein, as known to those skilled in the art. Method 500 may further include, at step 504, selecting a recommended action based on the count of low alarms triggered within the time period. The method 500 may further include, at step 508, storing the indicator of the recommended action in computer memory for output to a computing device that administers the medication, such as the user interface device 200 or the medication delivery device 152. The user interface device 200 may use the indicator of the recommended action to control a user interface, for example, by causing a human-readable representation of the indicator to appear on a display or by generating an audio output that expresses the indicator in a human language. The medication delivery device 152 may use the indicator to adjust or maintain the next associated dose administration.

[0110] In some embodiments, as shown in FIG. 8B , the administration guidance application may perform a low alarm frequency analysis in addition to the glucose pattern analysis to determine a recommended insulin dose setting. Method 520 may include, at step 522, determining a glucose pattern type for each TOD period of the plurality of TOD periods. Method 520 may further include, at step 524, determining a count value of low alarms triggered within a period of time. The algorithm may receive as input time-correlated analyte data generated by a sensor control device worn by the patient over the analysis period and / or may receive a log or count value of low alarms or detected events that meet a low alarm condition. Method 520 may further include, at step 526, selecting a recommended action based on the glucose pattern type and the count value of low alarms triggered within a period of time. Method 520 may further include, at step 528, storing an indicator of the recommended action in computer memory for output to a computing device that administers the medication, such as user interface device 200 or medication delivery device 152. The user interface device 200 can use the indicator of the recommended action to control a user interface, for example, by causing a human-readable representation of the indicator to appear on a display or by generating an audio output that represents the indicator in a human language. The medication delivery device 152 can use the indicator to adjust or maintain the next associated dose administration.

[0111] In some embodiments, as seen in FIG. 9 , the administration guidance application may perform a low alarm frequency analysis and a glucose dysregulation analysis in addition to a glucose pattern analysis to determine recommended insulin dose settings. Method 540 may include, at step 542, determining a glucose pattern type for each TOD period of the plurality of TOD periods. Method 540 may further include, at step 544, determining a measure of glucose dysregulation for each TOD period of the plurality of TOD periods by executing an algorithm that receives as input time-correlated analyte data generated by a sensor control device worn by the patient over the analysis period. Method 540 may further include, at step 546, determining a count value of low alarms triggered within a period. The algorithm may receive as input time-correlated analyte data generated by a sensor control device worn by the patient over the analysis period and / or may receive a log or count value of low alarms or detected events that meet a low alarm condition. Method 540 may further include selecting a recommended action based on the glucose pattern type, the count of low alarms triggered within a period of time, and the measure of glucose dysregulation, at step 548. Method 540 may further include storing an indicator of the recommended action in computer memory, at step 548, for output to a computing device that administers the medication, such as user interface device 200 or medication delivery device 152. User interface device 200 may use the indicator of the recommended action to control a user interface, for example, by causing a human-readable representation of the indicator to appear on a display or by generating an audio output that represents the indicator in a human language. Medication delivery device 152 may use the indicator to adjust or maintain the next associated dose administration.

[0112] The basal dose titration algorithm may have the following rules: If there is a low pattern in any TOD period or the frequency of low events is greater than 2 days with low events in the past 7 days, the basal dose will be reduced.

[0113] Otherwise, if there is a high pattern in any TOD period and no TOD period has a moderate risk of hypoglycemia, increase the basal dose. Instead, if there is a high pattern during any TOD period, it provides a notification that other treatment modifications may be necessary.

[0114] Otherwise, it provides notification that the patient is maintaining good glucose control. The MDI titration method may similarly use the low and high event frequency metrics in conjunction with pattern analysis to determine whether to increase or decrease the dose associated with a TOD. For example, for a particular TOD, if the pattern is a low pattern or if the low event frequency during that TOD period exceeds a threshold, the dose is decreased.

[0115] It should be noted that low event frequency and high event frequency can be incorporated into any form of administration titration. For example, there are currently commonly known methods for titrating basal insulin based on standard time-in-range (TIR), time-above-range (TAR), and time-below-range (TBR) glucose metrics. Low event frequency can be included in the titration logic so that the dose is decreased if the TBR threshold or a low event frequency threshold is exceeded. Otherwise, the dose is increased if the TAR threshold or a high event frequency threshold is exceeded. Similar logic combinations can be made for MDI administration titration algorithms.

[0116] Optimal Control Optimal control can be detected when the dose setting no longer tends to trend in a particular direction. For example, the system may determine that optimal control has been achieved when the same dose is repeated a minimum number of times within a certain period of time or across several dose setting changes (e.g., the same dose is repeated three times across eight dose setting changes). Alternatively, the system may determine that optimal control has been achieved when the output recommended dose changes alternate a consecutive number of times, for example, alternating up, down, up, or alternating down, up, down. An alternative consecutive number of recommended changes may be at least three, alternatively at least four, alternatively at least five, or alternatively at least six.

[0117] 10 illustrates an exemplary method for determining whether optimal control has been achieved and no further titration recommendations should be made. In method 560, in step 562, a plurality of recommended insulin dose recommendations are determined based on a hypoglycemic risk analysis. The hypoglycemic risk analysis can be based on known methods, including glucose pattern analysis, glucose dysregulation analysis, low alarm frequency analysis, and combinations thereof, as described elsewhere herein. In step 564, the system can determine whether no further titration of insulin doses should be recommended based on the analysis of the plurality of recommended insulin doses. If it is determined that further titration should be made, the method can return to step 562. If it is determined that no further titration should be made, an indication that titration optimization has been achieved can be output in step 566.

[0118] In other embodiments, the measure of optimal glucose control may be based on an analysis of the distribution of central tendency, eg, the mean or median, a measure of variability such as variance, or percentile values. Once the system determines that optimal control has been achieved, more sophisticated optimization detection methods may be utilized.

[0119] Recommended Actions The administration guidance application may store and / or output various recommended actions depending on the analysis results. In one embodiment, the administration guidance application may store and / or output the recommended dose change as a percentage of the current dose. Alternatively, the administration guidance application may store and / or output the recommended dose change in units of insulin. In another embodiment, the administration guidance application may store and / or output the recommended dose in units. The dose setting amount may vary depending on the magnitude of the dysregulation, such that larger changes may be recommended if the user's glucose metrics are far from optimal.

[0120] In some embodiments, the dosing guidance application may recommend adding a new medication class to a user's treatment. For example, a person may have a high median glucose, suggesting a need for an increased basal insulin dose, while simultaneously exhibiting high variability below the median. This high variability below the median means that an increased basal insulin dose is likely to induce instances of hypoglycemia. Therefore, despite the high median glucose, the basal dose cannot be titrated. Conversely, decreasing the dose should not be attempted, as it would also induce an increase in the median glucose. Thus, in situations where basal insulin can no longer be titrated due to the presence of high glucose variability, the system may recommend the use of an additional glucose-regulating medication. The system may also require the user to enter weight. Using the user's weight information, the system can track the weight-normalized basal insulin dose (units: U / kg). Many healthcare providers believe that a person's basal insulin dose should not exceed 0.5 U / kg. If the basal dose titration exceeds this upper threshold, this may also be a useful criterion for recommending the initiation of a new glucose-regulating therapy.

[0121] The dosing guidance application may determine that optimal glucose control is occurring. Optimal control may be detected when the dose setting no longer trends in a particular direction. Optimal control may also be detected when the same dose is repeated several times (e.g., three times in eight dose setting changes), where the dose is output after alternating increase, decrease, increase (or decrease, increase, decrease). More sophisticated optimization detection methods are contemplated. Measures of optimal glucose control may follow central tendency, such as the mean or median, or variability measures, such as the distribution of variance or percentile values.

[0122] Different outputs may be displayed depending on the recipient. Outputs to the healthcare professional may include recommending the next dose change, indicating that the maximum recommended dose has been reached, that optimal titration has been achieved and the patient is in good glucose control, or that optimal titration has been achieved but the patient remains in poor glucose control and intensification of therapy may be required. Outputs to the user may include a recommended dose change.

[0123] It should be noted that all features, elements, components, functions, and steps described with respect to all embodiments provided herein are intended to be freely combinable and interchangeable with those of any other embodiments. Even if a feature, element, component, function, or step is described with respect to only one embodiment, it should be understood that that feature, element, component, function, or step can be used in conjunction with all other embodiments described herein, unless expressly stated otherwise. Therefore, this paragraph serves as a prior basis and written support for the introduction of claims that combine features, elements, components, functions, and steps from different embodiments or substitute features, elements, components, functions, and steps of one embodiment for features, elements, components, functions, and steps of another embodiment, whenever the following description does not explicitly state that such combinations or substitutions are possible in a particular instance. Accordingly, the foregoing descriptions of specific embodiments of the disclosed subject matter have been presented for purposes of illustration and description. It is expressly recognized that an explicit enumeration of all possible combinations and permutations would be extremely burdensome, especially considering that the permissibility of each and every such combination and permutation would be readily recognized by one of ordinary skill in the art.

[0124] While the embodiments are amenable to various modifications and alternative forms, specific examples thereof are shown in the drawings and described in detail herein. It will be apparent to those skilled in the art that various modifications and variations can be made in the methods and systems of the disclosed subject matter without departing from the spirit or scope of the disclosed subject matter. Accordingly, the disclosed subject matter is intended to include modifications and variations that come within the scope of the appended claims and their equivalents. Furthermore, any feature, function, step, or element of the embodiments may be recited or added to the claims, and any feature, function, step, or element not within the scope of the claims may be recited as a negative limitation defining the scope of the claims.

[0125] In many embodiments, a method for determining dose settings for basal insulin administration includes: determining, by at least one processor, an analyte pattern type for each of a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated analyte data acquired over an analysis period; executing, by the at least one processor, a recommendation algorithm to select a recommended action based on the analyte pattern type; and storing, by the at least one processor, an indicator of the recommended action in computer memory for output.

[0126] In some embodiments, the recommended treatment is a recommended basal dose. In some embodiments, the method further includes at least one processor determining a measure of glucose dysregulation for a plurality of TOD periods, and a recommended action is selected based on at least the test substance pattern type and the measure of glucose dysregulation. In some embodiments, the measure of glucose dysregulation is determined by determining the number of times the test substance value exceeds or falls below a threshold crossing value in a period of time. In some embodiments, the measure of glucose dysregulation is determined by determining the duration of being above or below a threshold value in a period of time. In some embodiments, the measure of glucose dysregulation is determined by determining an area above or below a threshold area value in a period of time. In some embodiments, the measure of glucose dysregulation is determined by determining the number of days in a period of time during which a minimum number of instances of glucose dysregulation occurred.

[0127] In some embodiments, the method further includes at least one processor determining a frequency of low glucose alarms within a period of time, and a recommended action is selected based on at least the analyte pattern type and the frequency of low glucose alarms. In some embodiments, the at least one processor determining a frequency of low glucose alarms within a period of time includes determining whether a low glucose alarm has been triggered more than a threshold number of times.

[0128] In some embodiments, the method further includes the at least one processor outputting a recommended action. In some embodiments, the recommended action is a change to the next recommended dose. In some embodiments, the change is a percentage of the current dose. In some embodiments, the change is a dose value in units. In some embodiments, the recommended action is a recommendation to add a new medication. In some embodiments, the recommended action is output to a healthcare professional. In some embodiments, the recommended action is output to a user. In some embodiments, the recommended action is an indication that the maximum recommended dose has been reached.

[0129] In some embodiments, the recommended action is an indication that optimization in dosing has been achieved. In some embodiments, the recommended action further indicates that the user is in good glucose control. In some embodiments, the recommended action further indicates that the user remains in poor glucose control. In some embodiments, the recommended action further indicates that intensification of therapy may be required.

[0130] In some embodiments, selecting the recommended action is based on the test substance pattern type and an additional input. In some embodiments, the additional input includes the user's weight. In some embodiments, the additional input includes insulin administration data including dosage amounts and corresponding administration times. In some embodiments, the additional input includes a meal log. In some embodiments, the additional input includes an exercise log.

[0131] In many embodiments, a system for determining a recommended administration dose includes an input configured to receive time-correlated analyte data for a patient acquired over an analysis period; one or more processors coupled to the input; and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to determine an analyte pattern type for each of a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated analyte data acquired over the analysis period; select a recommended action based on the analyte pattern type; and store an indicator of the recommended action in computer memory for output.

[0132] In some embodiments, the recommended treatment is a recommended basal dose. In some embodiments, the instructions further cause the one or more processors to determine a measure of glucose dysregulation for a plurality of TOD periods, and the recommended action is selected based on at least the analyte pattern type and the measure of glucose dysregulation. In some embodiments, the measure of glucose dysregulation is determined by determining the number of times the analyte value exceeds or falls below a threshold crossing value in a period of time. In some embodiments, the measure of glucose dysregulation is determined by determining the duration of being above or below a threshold value in a period of time. In some embodiments, the measure of glucose dysregulation is determined by determining an area above or below a threshold area value in a period of time. In some embodiments, the measure of glucose dysregulation is determined by determining the number of days in a period of time during which a minimum number of instances of glucose dysregulation occurred.

[0133] In some embodiments, the instructions further cause the one or more processors to determine a frequency of low glucose alarms within a time period, and a recommended action is selected based on at least the analyte pattern type and the frequency of low glucose alarms. In some embodiments, the one or more processors determining the frequency of low glucose alarms within a time period includes determining whether a low glucose alarm has been triggered more than a threshold number of times.

[0134] In some embodiments, the instructions further cause the one or more processors to output a recommended action. In some embodiments, the recommended action is a change to the next recommended dose. In some embodiments, the change is a percentage of the current dose. In some embodiments, the change is a dose value in units.

[0135] In some embodiments, the recommended action is a recommendation to add a new medication. In some embodiments, the recommended action is output to a healthcare professional. In some embodiments, the recommended action is output to a user. In some embodiments, the recommended action is an indication that the maximum recommended dose has been reached.

[0136] In some embodiments, the recommended action is an indication that optimization in dosing has been achieved. In some embodiments, the recommended action further indicates that the user is in good glucose control. In some embodiments, the recommended action further indicates that the user remains in poor glucose control. In some embodiments, the recommended action further indicates that intensification of therapy may be required.

[0137] In some embodiments, the instructions cause the one or more processors to select a recommended action based on the analyte pattern type and the additional input. In some embodiments, the additional input includes a user's weight. In some embodiments, the additional input includes insulin administration data including a dosage amount and a corresponding administration time. In some embodiments, the additional input includes a meal log. In some embodiments, the additional input includes an exercise log.

[0138] In many embodiments, a method for determining dose settings for insulin administration includes: determining, by at least one processor, a measure of glucose dysregulation for a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated test substance data acquired over an analysis period; executing, by the at least one processor, a recommendation algorithm to select a recommended action based on the measure of glucose dysregulation; and storing, by the at least one processor, an indicator of the recommended action in computer memory for output.

[0139] In some embodiments, the measure of glucose dysregulation is determined by determining the number of times the analyte value exceeds or falls below a threshold crossing value over a period of time. In some embodiments, the measure of glucose dysregulation is determined by determining the duration above or below a threshold value over a period of time.

[0140] In some embodiments, a measure of glucose dysregulation is determined by determining areas above or below a threshold area value over a period of time. In some embodiments, the measure of glucose dysregulation is determined by determining the number of days in the period during which a minimum number of instances of glucose dysregulation occurred.

[0141] In some embodiments, the method further includes determining an analyte pattern type for a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated analyte data acquired over the analysis period, and a recommended action is selected based on at least the measure of glucose dysregulation and the determined analyte pattern.

[0142] In some embodiments, the method further includes at least one processor determining a frequency of low glucose alarms within a period of time, and a recommended action is selected based on at least the measure of glucose dysregulation and the frequency of the low glucose alarms.

[0143] In many embodiments, a system for determining recommended drug doses includes an input configured to receive time-correlated analyte data for a patient acquired over an analysis period; one or more processors coupled to the input; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to determine a measure of glucose dysregulation for a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated analyte data acquired over the analysis period; select a recommended action based on the measure of glucose dysregulation; and store an indicator of the recommended action in computer memory for output.

[0144] In some embodiments, the measure of glucose dysregulation is determined by determining the number of times the analyte value exceeds or falls below a threshold crossing value over a period of time. In some embodiments, the measure of glucose dysregulation is determined by determining the duration above or below a threshold value over a period of time.

[0145] In some embodiments, a measure of glucose dysregulation is determined by determining areas above or below a threshold area value over a period of time. In some embodiments, the measure of glucose dysregulation is determined by determining the number of days in the period during which a minimum number of instances of glucose dysregulation occurred.

[0146] In some embodiments, the instructions further cause the one or more processors to determine an analyte pattern type for a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated analyte data acquired over an analysis period, and a recommended action is selected based on at least the measure of glucose dysregulation and the determined analyte pattern.

[0147] In some embodiments, the instructions further cause the one or more processors to determine a frequency of low glucose alarms within a period of time, and a recommended response is selected based on at least the measure of glucose dysregulation and the frequency of the low glucose alarms.

[0148] In many embodiments, a method for determining a dose setting for insulin administration includes the steps of: at least one processor determining a frequency of low glucose alarms within a period of time; the at least one processor executing a recommendation algorithm to select a recommended action based on the frequency of the low glucose alarms; and the at least one processor storing an indicator of the recommended action in computer memory for output.

[0149] In some embodiments, the step of at least one processor determining the frequency of low glucose alarms within the period of time includes determining whether the low glucose alarm has been triggered more than a threshold number of times.

[0150] In some embodiments, the method further includes the at least one processor outputting a recommended action. In some embodiments, the method further includes determining an analyte pattern type for a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated analyte data acquired over the analysis period, and a recommended action is selected based on at least the frequency of low glucose alarms and the determined analyte pattern.

[0151] In some embodiments, the method further includes at least one processor determining a measure of glucose dysregulation for the plurality of TOD periods, and a recommended action is selected based on at least the frequency of low glucose alarms and the measure of glucose dysregulation.

[0152] In many embodiments, a system for determining recommended drug dosages includes an input configured to receive time-correlated test substance data for a patient obtained over an analysis period or a count of the number of low alarms over the analysis period, one or more processors coupled to the input, and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to determine a frequency of low glucose alarms within a period of time, select a recommended action based on the frequency of the low glucose alarms, and store an indicator of the recommended action in computer memory for output.

[0153] In some embodiments, the one or more processors determining the frequency of low glucose alarms within the period of time includes determining whether the low glucose alarm has been triggered more than a threshold number of times.

[0154] In some embodiments, the instructions further cause the one or more processors to output a recommended action. In some embodiments, the instructions further cause the one or more processors to determine an analyte pattern type for a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated analyte data acquired over an analysis period, and a recommended action is selected based on at least the frequency of low glucose alarms and the determined analyte pattern.

[0155] In some embodiments, the instructions further cause the one or more processors to determine a measure of glucose dysregulation for a plurality of TOD periods, and a recommended action is selected based on at least the frequency of low glucose alarms and the measure of glucose dysregulation.

[0156] In many embodiments, a method for managing dose setting for insulin administration includes steps of: at least one processor determining a plurality of recommended insulin doses based on a hypoglycemia risk analysis; at least one processor determining whether further dose setting of insulin doses should not be recommended based on the plurality of recommended insulin doses; and at least one processor outputting an indication that dose setting optimization has been achieved.

[0157] In some embodiments, the step of determining whether further titration of the insulin dose should not be recommended comprises determining whether the count value of the same dose in the plurality of recommended insulin doses is above a threshold value.

[0158] In some embodiments, the plurality of recommended insulin doses includes a portion of the most recently output recommendations, and determining whether further titration of the insulin dose should not be recommended includes determining whether the portion of the most recently output recommendations is trending upward or downward.

[0159] In some embodiments, the plurality of recommended insulin doses includes a portion of most recently output recommendations, and the step of determining whether further titration of insulin doses should not be recommended includes determining whether the amounts of consecutive doses in the portion of most recently output recommendations form an alternating pattern.

[0160] In some embodiments, the multiple recommended insulin doses are multiple recommended basal insulin doses. In some embodiments, the multiple recommended insulin doses are multiple recommended bolus insulin doses.

[0161] In some embodiments, the hypoglycemia risk analysis includes at least one processor determining an analyte pattern type for each of a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated analyte data acquired over the analysis period.

[0162] In some embodiments, the hypoglycemia risk analysis includes at least one processor determining a measure of glucose dysregulation for multiple time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated analyte data acquired over an analysis period.

[0163] In some embodiments, the hypoglycemia risk analysis includes at least one processor determining a frequency of low glucose alarms within a period of time. In some embodiments, the hypoglycemia risk analysis includes determining an analyte pattern type for each of a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated analyte data acquired over the analysis period, and determining at least one of determining a measure of glucose dysregulation and determining a frequency of low glucose alarms within a period.

[0164] In many embodiments, a system for managing titration for insulin administration includes an input configured to receive administration data including data related to a plurality of doses administered during a period of time, one or more processors coupled to the input, and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: determine a plurality of recommended insulin doses based on a hypoglycemia risk analysis; determine whether further titration of insulin doses should not be recommended based on the plurality of recommended insulin doses; and output an indication that titration optimization has been achieved.

[0165] In some embodiments, the one or more processors determine whether further titration of an insulin dose should not be recommended by determining whether the count values ​​of the same dose in the multiple recommended insulin doses are above a threshold.

[0166] In some embodiments, the plurality of recommended insulin doses includes a portion of the most recently output recommendations, and determining whether further titration of the insulin doses should not be recommended includes determining whether the portion of the most recently output recommendations is trending upward or downward.

[0167] In some embodiments, the plurality of recommended insulin doses includes a portion of the most recently output recommendations, and determining whether further titration of the insulin doses should not be recommended includes determining whether the amounts of consecutive doses in the portion of the most recently output recommendations form an alternating pattern.

[0168] In some embodiments, the multiple recommended insulin doses are multiple recommended basal insulin doses. In some embodiments, the multiple recommended insulin doses are multiple recommended bolus insulin doses.

[0169] In some embodiments, the hypoglycemia risk analysis includes at least one processor determining an analyte pattern type for each of a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated analyte data acquired over the analysis period.

[0170] In some embodiments, the hypoglycemia risk analysis includes at least one processor determining a measure of glucose dysregulation for multiple time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated analyte data acquired over an analysis period.

[0171] In some embodiments, the hypoglycemia risk analysis includes at least one processor determining a frequency of low glucose alarms within a period of time. In some embodiments, the hypoglycemia risk analysis includes determining an analyte pattern type for each of a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated analyte data acquired over the analysis period, and determining at least one of determining a measure of glucose dysregulation and determining a frequency of low glucose alarms within the period.

[0172] Terms Exemplary embodiments are described below in numbered clauses. Section 1 1. A method for determining a titration for basal insulin administration, comprising: determining an analyte pattern type for each of a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm with at least one processor that receives as input time-correlated analyte data for the patient acquired over the analysis period; the at least one processor executing a recommendation algorithm to select a recommended action based on the analyte pattern type; and wherein the at least one processor stores the indicator of the recommended action in computer memory for output.

[0173] Section 2 2. The method of claim 1, wherein the recommended action is a recommended basal dose. Section 3 at least one processor further determining a measure of glucose dysregulation for the plurality of TOD periods; 3. The method of claim 1 or 2, wherein the recommended action is selected based on at least the test substance pattern type and the measure of glucose dysregulation.

[0174] Section 4 4. The method of any one of claims 1 to 3, wherein the measure of glucose dysregulation is determined by determining the number of times the test substance value exceeds or falls below a threshold crossing value over a period of time.

[0175] Section 5 5. The method of any one of paragraphs 1 to 4, wherein the measure of glucose dysregulation is determined by determining the duration above or below a threshold value over a period of time.

[0176] Section 6 6. The method of any one of clauses 1 to 5, wherein the measure of glucose dysregulation is determined by determining areas above or below a threshold area value over a period of time.

[0177] Section 7 7. The method of any one of paragraphs 1 to 6, wherein the measure of glucose dysregulation is determined by determining the number of days in the period during which a minimum number of instances of glucose dysregulation occurred.

[0178] Section 8 The method further includes at least one processor determining a frequency of low glucose alarms within a period of time; 8. The method of any one of paragraphs 1 to 7, wherein the recommended action is selected based on at least the analyte pattern type and the frequency of the low glucose alarm.

[0179] Section 9 9. The method of any one of paragraphs 1 to 8, wherein the step of at least one processor determining the frequency of low glucose alarms within a period of time includes determining whether the low glucose alarm has been triggered more than a threshold number of times.

[0180] Section 10 10. The method according to any one of claims 1 to 9, further comprising the step of the at least one processor outputting the recommended action.

[0181] Section 11 11. The method of any one of paragraphs 1 to 10, wherein the recommended action is a change to the next recommended administration.

[0182] Section 12 12. The method of any one of paragraphs 1 to 11, wherein the change is a percentage of the current dosage.

[0183] Section 13 13. The method of any one of paragraphs 1 to 12, wherein the change is a unit dose value.

[0184] Section 14 14. The method according to any one of items 1 to 13, wherein the recommended action is to recommend the addition of a new drug.

[0185] Section 15 15. The method according to any one of items 1 to 14, wherein the recommended measures are output to a medical professional.

[0186] Section 16 16. The method according to any one of paragraphs 1 to 15, wherein the recommended measures are output to a user.

[0187] Section 17 17. The method of any one of paragraphs 1 to 16, wherein the recommended action is an indication that the maximum recommended dose has been reached.

[0188] Section 18 18. The method of any one of paragraphs 1 to 17, wherein the recommended action is an indication that optimization in dose titration has been achieved.

[0189] Section 19 19. The method of any one of paragraphs 1 to 18, wherein the recommended action further indicates that the user has good glucose control.

[0190] Section 20 20. The method of any one of paragraphs 1 to 19, wherein the recommended action further indicates that the user remains in poor glucose control.

[0191] Section 21 21. The method of any one of paragraphs 1 to 20, wherein the recommended action further indicates that intensified treatment may be required.

[0192] Section 22 22. The method of any one of paragraphs 1 to 21, wherein selecting the recommended action is based on the analyte pattern type and additional input.

[0193] Section 23 23. The method of any one of paragraphs 1 to 22, wherein the additional input includes a user's weight.

[0194] Section 24 24. The method of any one of clauses 1 to 23, wherein the additional input comprises insulin administration data including dosage amounts and corresponding administration times.

[0195] Section 25 25. The method of any one of paragraphs 1 to 24, wherein the additional input includes a meal log.

[0196] Section 26 26. The method of any one of paragraphs 1 to 25, wherein the additional input includes a log of exercise.

[0197] Section 27 1. A system for determining a recommended drug dosage, comprising: an input configured to receive time-correlated analyte data for a patient acquired over an analysis period; one or more processors coupled to the input; and a memory holding instructions that, when executed by the one or more processors, cause the one or more processors to: determining an analyte pattern type for each of a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input time-correlated analyte data for the patient acquired over the analysis period; selecting a recommended action based on said test substance pattern type; and storing the indicator of the recommended action in a computer memory for output.

[0198] Section 28 28. The system of clause 27, wherein the recommended action is a recommended basal dose. Section 29 The instructions further include causing the one or more processors to: determining a measure of glucose dysregulation for said plurality of TOD periods; 29. The system of claim 27 or 28, wherein the recommended action is selected based on at least the test substance pattern type and the measure of glucose dysregulation.

[0199] Section 30 30. The system of any one of paragraphs 27 to 29, wherein the measure of glucose dysregulation is determined by determining the number of times the test substance value exceeds or falls below a threshold crossing value over a period of time.

[0200] Section 31 31. The system of any one of paragraphs 27 to 30, wherein the measure of glucose dysregulation is determined by determining the duration above or below a threshold value over a period of time.

[0201] Section 32 32. The system of any one of clauses 27 to 31, wherein the measure of glucose dysregulation is determined by determining areas above or below threshold area values ​​over a period of time.

[0202] Section 33 33. The system of any one of paragraphs 27 to 32, wherein the measure of glucose dysregulation is determined by determining the number of days in a period during which a minimum number of instances of glucose dysregulation occurred.

[0203] Section 34 The instructions further include causing the one or more processors to: determining a frequency of low glucose alarms within a period of time; 34. The system of any one of paragraphs 27 to 33, wherein the recommended action is selected based on at least the test substance pattern type and the frequency of the low glucose alarm.

[0204] Section 35 The system of any one of paragraphs 27 to 34, wherein the one or more processors determining the frequency of the low glucose alarm within the period includes determining whether the low glucose alarm has been triggered more than a threshold number of times.

[0205] Section 36 36. The system of any one of clauses 27 to 35, wherein the set of instructions further cause the one or more processors to output the recommended action.

[0206] Section 37 37. The system according to any one of paragraphs 27 to 36, wherein the recommended action is a change to the next recommended administration.

[0207] Section 38 38. The system of any one of paragraphs 27 to 37, wherein the change is a percentage of the current dosage.

[0208] Section 39 39. The system according to any one of paragraphs 27 to 38, wherein the change is a dosage value in units.

[0209] Section 40 40. The system according to any one of items 27 to 39, wherein the recommended action is to recommend the addition of a new drug.

[0210] Section 41 41. The system according to any one of items 27 to 40, wherein the recommended measures are output to a medical professional.

[0211] Section 42 42. The system according to any one of paragraphs 27 to 41, wherein the recommended measures are output to a user.

[0212] Section 43 43. The system of any one of paragraphs 27 to 42, wherein the recommended action is an indication that the maximum recommended dose has been reached.

[0213] Section 44 44. The system of any one of paragraphs 27 to 43, wherein the recommended action is an indication that optimal dose setting has been achieved.

[0214] Section 45 The system of any one of paragraphs 27 to 44, wherein the recommended action further indicates that the user is in good glucose control.

[0215] Section 46 The system of any one of paragraphs 27 to 45, wherein the recommended action further indicates that the user remains in poor glucose control.

[0216] Section 47 The system of any one of claims 27 to 46, wherein the recommended action further indicates that intensified treatment may be required.

[0217] Section 48 The system of any one of claims 27 to 47, wherein the set of instructions causes the one or more processors to select the recommended action based on the test substance pattern type and additional input.

[0218] Section 49 The system of any one of paragraphs 27 to 48, wherein the additional input includes the user's weight. Section 50 49. The system of claim 27, wherein the additional input includes insulin administration data including dosage amounts and corresponding administration times.

[0219] Section 51 The system of any one of paragraphs 27 to 50, wherein the additional input includes a meal log record. Section 52 The system of any one of paragraphs 27 to 51, wherein the additional input includes a log of exercise.

[0220] Section 53 1. A method for determining a titration for insulin administration, comprising: determining a measure of glucose dysregulation for a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm in which at least one processor receives as input the patient's time-correlated analyte data acquired over an analysis period; the at least one processor executing a recommendation algorithm to select a recommended action based on the measure of glucose dysregulation; and wherein the at least one processor stores the indicator of the recommended action in computer memory for output.

[0221] Section 54 54. The method of clause 53, wherein the measure of glucose dysregulation is determined by determining the number of times the test substance value exceeds or falls below a threshold crossing value within a period of time.

[0222] Section 55 55. The method of clause 53 or clause 54, wherein the measure of glucose dysregulation is determined by determining the duration above or below a threshold value over a period of time.

[0223] Section 56 56. The method of any one of clauses 53 to 55, wherein the measure of glucose dysregulation is determined by determining the area above or below a threshold area value over a period of time.

[0224] Section 57 57. The method of any one of clauses 53 to 56, wherein the measure of glucose dysregulation is determined by determining the number of days in the period during which a minimum number of instances of glucose dysregulation occurred.

[0225] Section 58 determining an analyte pattern type for a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm in which the at least one processor receives as input time-correlated analyte data for the patient acquired over the analysis period; 58. The method of any one of items 53 to 57, wherein the recommended action is selected based on at least the measure of glucose dysregulation and the determined test substance pattern.

[0226] Section 59 The method further includes at least one processor determining a frequency of low glucose alarms within a period of time; 59. The method of any one of paragraphs 53 to 58, wherein the recommended action is selected based on at least the measure of glucose dysregulation and the frequency of the low glucose alarms.

[0227] Section 60 1. A system for determining a recommended drug dosage, comprising: an input configured to receive time-correlated analyte data for a patient acquired over an analysis period; one or more processors coupled to the input; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: determining a measure of glucose dysregulation for a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input time-correlated analyte data of the patient acquired over an analysis period; selecting a recommended action based on said measure of glucose dysregulation; and storing the indicator of the recommended action in a computer memory for output.

[0228] Section 61 61. The system of clause 60, wherein the measure of glucose dysregulation is determined by determining the number of times the test substance value exceeds or falls below a threshold crossing value over a period of time.

[0229] Section 62 62. The system of clause 60 or 61, wherein the measure of glucose dysregulation is determined by determining the duration above or below a threshold value over a period of time.

[0230] Section 63 63. The system of any one of clauses 60 to 62, wherein the measure of glucose dysregulation is determined by determining areas above or below threshold area values ​​over a period of time.

[0231] Section 64 64. The system of any one of paragraphs 60 to 63, wherein the measure of glucose dysregulation is determined by determining the number of days in a period during which a minimum number of instances of glucose dysregulation occurred.

[0232] Section 65 The instructions further include causing the one or more processors to: determining an analyte pattern type for a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input time-correlated analyte data for the patient acquired over an analysis period; 65. The system of any one of items 60 to 64, wherein the recommended action is selected based on at least the measure of glucose dysregulation and the determined test substance pattern.

[0233] Section 66 The instructions further include causing the one or more processors to: determining a frequency of low glucose alarms within a period of time; 66. The system of any one of paragraphs 60 to 65, wherein the recommended action is selected based on at least the measure of glucose dysregulation and the frequency of the low glucose alarms.

[0234] Section 67 1. A method for determining a titration for insulin administration, comprising: at least one processor determining a frequency of low glucose alarms within a period of time; the at least one processor executing a recommendation algorithm to select a recommended action based on the frequency of the low glucose alarms; and wherein the at least one processor stores the indicator of the recommended action in computer memory for output.

[0235] Section 68 68. The method of clause 67, wherein the step of at least one processor determining the frequency of low glucose alarms within the period of time includes determining whether low glucose alarms have been triggered more than a threshold number of times.

[0236] Section 69 69. The method of clause 67 or clause 68, further comprising the at least one processor outputting the recommended action.

[0237] Section 70 determining an analyte pattern type for a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm in which the at least one processor receives as input time-correlated analyte data for the patient acquired over the analysis period; 70. The method of any one of paragraphs 67 to 69, wherein the recommended action is selected based on at least the frequency of the low glucose alarms and the determined test substance pattern.

[0238] Section 71 at least one processor further determining a measure of glucose dysregulation for the plurality of TOD periods; 71. The method of any one of clauses 67 to 70, wherein the recommended action is selected based on at least the frequency of the low glucose alarms and the measure of glucose dysregulation.

[0239] Section 72 1. A system for determining a recommended drug dosage, comprising: an input configured to receive time-correlated analyte data of the patient acquired over an analysis period or a count of the number of low alarms over the analysis period; one or more processors coupled to the input; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: determining a frequency of low glucose alarms within a period of time; selecting a recommended action based on the frequency of the low glucose alarm; and causing the indicator of the recommended action to be output to a computer memory.

[0240] Section 73 73. The system of claim 72, wherein the one or more processors determining the frequency of low glucose alarms within a period of time includes determining whether the low glucose alarm has been triggered more than a threshold number of times.

[0241] Section 74 74. The system of claim 72 or 73, wherein the instructions further cause the one or more processors to output the recommended action.

[0242] Section 75 The instructions further include causing the one or more processors to: determining an analyte pattern type for a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input time-correlated analyte data for the patient acquired over an analysis period; 75. The system of any one of paragraphs 72 to 74, wherein the recommended action is selected based on at least the frequency of the low glucose alarm and the determined test substance pattern.

[0243] Section 76 The instructions further include causing the one or more processors to: determining a measure of glucose dysregulation for said plurality of TOD periods; 76. The system of any one of paragraphs 72 to 75, wherein the recommended action is selected based on at least the frequency of the low glucose alarms and the measure of glucose dysregulation.

[0244] Section 77 1. A method for managing titration for insulin administration, comprising: determining, by at least one processor, a plurality of recommended insulin doses based on the hypoglycemia risk analysis; determining, by the at least one processor, whether further titration of insulin doses should not be recommended based on the plurality of recommended insulin doses; and the at least one processor outputs an indication that titration optimization has been achieved.

[0245] Section 78 78. The method of clause 77, wherein the step of determining whether further titration of the insulin dose should not be recommended comprises determining whether the count value of the same dose in the plurality of recommended insulin doses is above a threshold.

[0246] Section 79 79. The method of any one of paragraphs 77 to 78, wherein the plurality of recommended insulin doses includes a portion of the most recently output recommendations, and the step of determining whether further insulin dose setting should not be recommended includes determining whether the portion of the most recently output recommendations is trending upward or downward.

[0247] Section 80 79. The method of any one of paragraphs 77 to 79, wherein the plurality of recommended insulin doses comprises a portion of most recently output recommendations, and the step of determining whether further titration of the insulin dosage should not be recommended comprises determining whether the amounts of consecutive doses in the portion of most recently output recommendations form an alternating pattern.

[0248] Section 81 81. The method of any one of clauses 77 to 80, wherein the plurality of recommended insulin doses is a plurality of recommended basal insulin doses.

[0249] Section 82 82. The method of any one of clauses 77 to 81, wherein the plurality of recommended insulin doses is a plurality of recommended bolus insulin doses.

[0250] Section 83 83. The method of any one of clauses 77 to 82, wherein the hypoglycemia risk analysis includes determining a test substance pattern type for each of a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm in which at least one processor receives as input the patient's time-correlated test substance data taken over the analysis period.

[0251] Section 84 84. The method of any one of paragraphs 77 to 83, wherein the hypoglycemia risk analysis includes determining a measure of glucose dysregulation for a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm in which at least one processor receives as input the patient's time-correlated test substance data acquired over an analysis period.

[0252] Section 85 85. The method of any one of paragraphs 77 to 84, wherein the hypoglycemia risk analysis includes at least one processor determining a frequency of low glucose alarms within a period of time.

[0253] Section 86 86. The method of any one of paragraphs 77 to 85, wherein the hypoglycemia risk analysis includes determining a test substance pattern type for each of a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated test substance data acquired over an analysis period, and determining at least one of determining a measure of glucose dysregulation and determining a frequency of low glucose alarms within a period.

[0254] Section 87 1. A system for managing dose settings for insulin administration, comprising: an input configured to receive administration data including data regarding a plurality of doses administered over a period of time; one or more processors coupled to the input; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: determining multiple recommended insulin doses based on a hypoglycemic risk analysis; determining whether further titration of insulin doses should not be recommended based on the plurality of recommended insulin doses; The system causes the system to output an indication that titration optimization has been achieved.

[0255] Section 88 88. The system described in paragraph 87, wherein the one or more processors determine whether further titration of the insulin dose should not be recommended by determining whether the count value of the same dose in the plurality of recommended insulin doses exceeds a threshold.

[0256] Section 89 89. The system of claim 87 or 88, wherein the plurality of recommended insulin doses includes some of the most recently output recommendations, and the step of determining whether further titration of the insulin dose should not be recommended includes determining whether some of the most recently output recommendations are trending upward or downward.

[0257] Section 90 The system of any one of paragraphs 87 to 89, wherein the plurality of recommended insulin doses includes a portion of the most recently output recommendations, and the step of determining whether further titration of the insulin dosage should not be recommended includes determining whether the amounts of consecutive doses in the portion of the most recently output recommendations form an alternating pattern.

[0258] Section 91 91. The system of any one of items 87 to 90, wherein the plurality of recommended insulin doses is a plurality of recommended basal insulin doses.

[0259] Section 92 92. The system of any one of paragraphs 87 to 91, wherein the plurality of recommended insulin doses is a plurality of recommended bolus insulin doses.

[0260] Section 93 The system of any one of paragraphs 87 to 92, wherein the hypoglycemia risk analysis includes at least one processor executing a pattern analysis algorithm that receives as input the patient's time-correlated test substance data acquired over the analysis period, thereby determining a test substance pattern type for each of a plurality of time-of-day (TOD) periods.

[0261] Section 94 The system of any one of paragraphs 87 to 93, wherein the hypoglycemia risk analysis includes at least one processor executing a pattern analysis algorithm that receives as input the patient's time-correlated test substance data acquired over an analysis period, thereby determining a measure of glucose dysregulation for multiple time-of-day (TOD) periods.

[0262] Section 95 The system of any one of paragraphs 87 to 94, wherein the hypoglycemia risk analysis includes at least one processor determining the frequency of low glucose alarms within a period of time.

[0263] Section 96 The system of any one of paragraphs 87 to 95, wherein the hypoglycemia risk analysis includes determining a test substance pattern type for each of a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated test substance data acquired over an analysis period, and determining at least one of determining a measure of glucose dysregulation and determining a frequency of low glucose alarms within a period.

Claims

1. 1. A system for determining a recommended drug dosage, comprising: an input configured to receive time-correlated analyte data for a patient acquired over an analysis period; one or more processors coupled to the input; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: determining an analyte pattern type for each of a plurality of time-of-day (hereinafter TOD) periods by executing a pattern analysis algorithm that receives as input time-correlated analyte data for the patient acquired over the analysis period; selecting a recommended action based on said test substance pattern type; and storing the indicator of the recommended action in a computer memory for output.

2. The system of claim 1 , wherein the recommended action is a recommended basal dose.

3. The instructions further include causing the one or more processors to: determining a measure of glucose dysregulation for said plurality of TOD periods; The system of claim 1 , wherein the recommended action is selected based on at least the analyte pattern type and the measure of glucose dysregulation.

4. 4. The system of claim 3, wherein the measure of glucose dysregulation is determined by determining the number of times an analyte value exceeds or falls below a threshold crossing value over a period of time.

5. The system of claim 3 , wherein the measure of glucose dysregulation is determined by determining the duration above or below a threshold value over a period of time.

6. The system of claim 3 , wherein the measure of glucose dysregulation is determined by determining areas above or below threshold area values ​​over a period of time.

7. The system of claim 3 , wherein the measure of glucose dysregulation is determined by determining the number of days in a period during which a minimum number of instances of glucose dysregulation occurred.

8. The instructions further include causing the one or more processors to: determining a frequency of low glucose alarms within a period of time; The system of claim 1 , wherein the recommended action is selected based on at least the analyte pattern type and the frequency of the low glucose alarm.

9. 9. The system of claim 8, wherein the one or more processors determining the frequency of the low glucose alarm in the period includes determining whether the low glucose alarm has been triggered more than a threshold number of times.

10. The system of claim 1 , wherein the instructions further cause the one or more processors to output the recommended action.

11. The system of claim 10 , wherein the recommended action is a change to the next recommended administration.

12. 12. The system of claim 11, wherein the change is a percentage of the current dosage.

13. The system of claim 11 , wherein the change is a dose value in units.

14. The system of claim 10 , wherein the recommended action is to recommend adding a new medication.

15. The system of claim 10 , wherein the recommended action is output to a medical professional.

16. The system of claim 10 , wherein the recommended action is output to a user.

17. 11. The system of claim 10, wherein the recommended action is an indication that a maximum recommended dose has been reached.

18. 11. The system of claim 10, wherein the recommended action is an indication that optimization in dose titration has been achieved.

19. 20. The system of claim 18, wherein the recommended action further indicates that the user has good glucose control.

20. 20. The system of claim 18, wherein the recommended action further indicates that the user remains in poor glucose control.

21. 21. The system of claim 20, wherein the recommended action further indicates that increased therapy may be required.

22. The system of claim 2 , wherein the instructions cause the one or more processors to select the recommended action based on the analyte pattern type and additional inputs.

23. 23. The system of claim 22, wherein the additional input includes a user's weight.

24. 23. The system of claim 22, wherein the additional input includes insulin administration data including dosage amounts and corresponding administration times.

25. 23. The system of claim 22, wherein the additional input includes a meal log.

26. 23. The system of claim 22, wherein the additional input includes an exercise log.

27. 1. A method for determining a titration for basal insulin administration, comprising: determining an analyte pattern type for each of a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm with at least one processor that receives as input time-correlated analyte data for the patient acquired over the analysis period; the at least one processor executing a recommendation algorithm to select a recommended action based on the analyte pattern type; and wherein said at least one processor stores said indicator of the recommended action in computer memory for output.

28. 1. A system for determining a recommended drug dosage, comprising: an input configured to receive time-correlated analyte data for a patient acquired over an analysis period; one or more processors coupled to the input; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: determining a measure of glucose dysregulation for a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated analyte data acquired over an analysis period; selecting a recommended action based on said measure of glucose dysregulation; and storing the indicator of the recommended action in a computer memory for output.

29. 30. The system of claim 28, wherein the measure of glucose dysregulation is determined by determining the number of times an analyte value exceeds or falls below a threshold crossing value over a period of time.

30. 30. The system of claim 28, wherein the measure of glucose dysregulation is determined by determining the duration above or below a threshold value over a period of time.

31. 30. The system of claim 28, wherein the measure of glucose dysregulation is determined by determining areas above or below threshold area values ​​over a period of time.

32. 30. The system of claim 28, wherein the measure of glucose dysregulation is determined by determining the number of days in a period during which a minimum number of instances of glucose dysregulation occurred.

33. The instructions further include causing the one or more processors to: determining an analyte pattern type for a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input time-correlated analyte data for the patient acquired over an analysis period; 30. The system of claim 28, wherein the recommended action is selected based on at least the measure of glucose dysregulation and the determined analyte pattern.

34. The instructions further include causing the one or more processors to: determining a frequency of low glucose alarms within a period of time; 30. The system of claim 28, wherein the recommended action is selected based on at least the measure of glucose dysregulation and the frequency of the low glucose alarms.

35. 1. A method for determining a titration for insulin administration, comprising: at least one processor determining a frequency of low glucose alarms within a period of time; the at least one processor executing a recommendation algorithm to select a recommended action based on the frequency of the low glucose alarms; and wherein said at least one processor stores said indicator of the recommended action in computer memory for output.

36. 1. A system for determining a recommended drug dosage, comprising: an input configured to receive time-correlated analyte data of the patient acquired over an analysis period or a count of the number of low alarms over the analysis period; one or more processors coupled to the input; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: determining a frequency of low glucose alarms within a period of time; selecting a recommended action based on the frequency of the low glucose alarm; and storing the indicator of the recommended action in a computer memory for output.

37. 37. The system of claim 36, wherein the one or more processors determining the frequency of the low glucose alarm in the period includes determining whether the low glucose alarm has been triggered more than a threshold number of times.

38. 37. The system of claim 36, wherein the instructions further cause the one or more processors to output the recommended action.

39. The instructions further include causing the one or more processors to: determining analyte pattern types for a plurality of time-of-day (hereinafter TOD) periods by executing a pattern analysis algorithm that receives as input time-correlated analyte data for the patient acquired over an analysis period; 37. The system of claim 36, wherein the recommended action is selected based on at least the frequency of the low glucose alarms and the determined analyte pattern.

40. The instructions further include causing the one or more processors to: determining a measure of glucose dysregulation for said plurality of TOD periods; 37. The system of claim 36, wherein the recommended action is selected based on at least the frequency of the low glucose alarms and the measure of glucose dysregulation.

41. 1. A method for determining a titration for insulin administration, comprising: determining a measure of glucose dysregulation for a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm in which at least one processor receives as input the patient's time-correlated analyte data acquired over an analysis period; the at least one processor executing a recommendation algorithm to select a recommended action based on the measure of glucose dysregulation; and wherein said at least one processor stores said indicator of the recommended action in computer memory for output.

42. 1. A system for managing dose settings for insulin administration, comprising: an input configured to receive administration data including data regarding a plurality of doses administered over a period of time; one or more processors coupled to the input; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: determining multiple recommended insulin doses based on a hypoglycemic risk analysis; determining whether further titration of insulin doses should not be recommended based on the plurality of recommended insulin doses; The system causes the system to output an indication that titration optimization has been achieved.

43. 43. The system of claim 42, wherein the one or more processors determine whether further titration of the insulin dose should not be recommended by determining whether count values ​​of the same dose in the plurality of recommended insulin doses are above a threshold.

44. 43. The system of claim 42, wherein the plurality of recommended insulin doses includes a portion of most recently output recommendations, and determining whether further titration of the insulin doses should not be recommended includes determining whether the portion of most recently output recommendations is trending upward or downward.

45. 43. The system of claim 42, wherein the plurality of recommended insulin doses includes a portion of most recently output recommendations, and determining whether further titration of the insulin doses should not be recommended includes determining whether amounts of consecutive doses in the portion of the most recently output recommendations form an alternating pattern.

46. 43. The system of claim 42, wherein the plurality of recommended insulin doses is a plurality of recommended basal insulin doses.

47. 43. The system of claim 42, wherein the plurality of recommended insulin doses is a plurality of recommended bolus insulin doses.

48. 43. The system of claim 42, wherein the hypoglycemia risk analysis includes at least one processor executing a pattern analysis algorithm that receives as input the patient's time-correlated analyte data acquired over an analysis period to determine an analyte pattern type for each of a plurality of time-of-day (TOD) periods.

49. 43. The system of claim 42, wherein the hypoglycemia risk analysis includes at least one processor executing a pattern analysis algorithm that receives as input the patient's time-correlated analyte data acquired over an analysis period to determine a measure of glucose dysregulation for multiple time-of-day (TOD) periods.

50. 43. The system of claim 42, wherein the hypoglycemia risk analysis includes at least one processor determining a frequency of low glucose alarms within a period of time.

51. 43. The system of claim 42, wherein the hypoglycemia risk analysis includes determining a test substance pattern type for each of a plurality of time-of-day (TOD) periods by executing a pattern analysis algorithm that receives as input the patient's time-correlated test substance data acquired over an analysis period, and determining at least one of determining a measure of glucose dysregulation and determining a frequency of low glucose alarms within a period.

52. 1. A method for managing titration for insulin administration, comprising: determining, by at least one processor, a plurality of recommended insulin doses based on the hypoglycemic risk analysis; the at least one processor determining whether further titration of insulin doses should not be recommended based on the plurality of recommended insulin doses; and the at least one processor outputting an indication that titration optimization has been achieved.

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